Thursday, November 5, 2026

AI - Generative AI - LLMs - Applications in Software Development for Productivity and Quality Improvement - Industrial Engineering

Adapted from: License: CC BY-NC-SA 4.0, arXiv:2603.16975v1 [cs.SE] 17 Mar 2026

The State of Generative AI in Software Development: Insights from Literature and a Developer Survey

The references are given at the end of each application. You need to search and find the available content online.

The rapid advancement of Generative Artificial Intelligence is increasingly transforming software development practices across the Software Development Life Cycle.

Among AI-assisted development tools, conversational LLMs such as ChatGPT and assistants integrated directly in the development environment like GitHub Copilot, Amazon CodeWhisperer, JetBrains AI Assistant, and Tabnine have become widely adopted, supporting tasks such as code generation, explanation, and review (Sergeyuk et al., 2025).

Sergeyuk et al. (2025): A. Sergeyuk, Y. Golubev, T. Bryksin, and I. Ahmed

Using AI-based coding assistants in practice: State of affairs, perceptions, and ways forward.

Information and Software Technology 178, pp. 107610.

Empirical studies and field research consistently report productivity gains across development activities, particularly in code optimization (approximately 60 %), bug fixing (26 %), and documentation support (12 %) (Collante et al., 2025).

Collante et al. (2025): A. Collante, S. Abedu, S. Khatoonabadi, A. Abdellatif, E. Alor, and E. Shihab

The Impact of Large Language Models (LLMs) on Code Review Process.

arXiv. Note: arXiv:2508.11034 [cs]

Evidence further suggests that less experienced developers benefit disproportionately from AI assistance, achieving higher relative productivity improvements (Brynjolfsson et al., 2023; Dell’Acqua et al., 2023; Ng et al., 2024).

Brynjolfsson et al. (2023): E. Brynjolfsson, D. Li, and L. R. Raymond

Generative AI at work.

Dell’Acqua et al. (2023)

F. Dell’Acqua, E. McFowland, E. R. Mollick, H. Lifshitz-Assaf, K. Kellogg, S. Rajendran, L. Krayer, F. Candelon, and K. R. Lakhani

Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. SSRN Electronic Journal (en).

Ng et al. (2024): K. K. Ng, L. Fauzi, L. Leow, and J. Ng

Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development.

arXiv (en). Note: arXiv:2409.17434 [cs]

At the same time, studies indicate that code quality generally remains stable or improves despite reduced development time (Shihab et al., 2025; Yadav and Mondal, 2025).

Shihab et al. (2025)

M. I. H. Shihab, C. Hundhausen, A. Tariq, S. Haque, Y. Qiao, and B. W. Mulanda

The Effects of GitHub Copilot on Computing Students’ Programming Effectiveness, Efficiency, and Processes in Brownfield Coding Tasks.

In Proceedings of the 2025 ACM Conference on International Computing Education Research V.1,

Charlottesville USA, pp. 407–420.

Yadav and Mondal (2025)

D. Yadav and S. Mondal

Evaluating Pre-trained Large Language Models on Zero Shot Prompts for Parallelization of Source Code.

OSF.

Correctness cannot be guaranteed and long-term risks remain, making human validation and oversight essential (Yetiştiren et al., 2023; Atif et al., 2025).

Yetiştiren et al. (2023)

B. Yetiştiren, I. Özsoy, M. Ayerdem, and E. Tüzün

Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT.

arXiv (en).

Note: arXiv:2304.10778 [cs]

Atif et al. (2025)

M. Atif, K. Chopra, O. Kilic, T. Wang, Z. Dong, C. Leggett, M. Lin, P. Calafiura, and S. Habib

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics.

arXiv (en).

Note: arXiv:2508.16713 [cs]

Beyond individual productivity, industry reports highlight broader organizational implications, including accelerated prototyping, shorter time-to-market, and shifting accountability toward product and project leadership (Deniz et al., 2023; Gnanasambandam et al., 2025).

Gnanasambandam et al. (2025)

C. Gnanasambandam, M. Harrysson, and R. Singh

How an AI-enabled software product development life cycle will fuel innovation., Technical report McKinsey.

Deniz et al. (2023)

B. K. Deniz, C. Gnanasambandam, M. Harrysson, A. Hussin, and S. Srivastava

Unleash developer productivity with generative AI., Technical report, McKinsey.

Generative Artificial Intelligence Applications in the Software Development Lifecycle

Planning.

In the planning phase, Generative Artificial Intelligence supports the creation of foundational project artifacts such as scope definitions, timelines, milestone structures, role descriptions, communication plans, and risk assessments (Barcaui and Monat, 2023; Hughes et al., 2025; Maggoo et al., 2025).

Barcaui and Monat (2023): A. Barcaui and A. Monat

Who is better in project planning?Generative artificial intelligence or project managers?., Project Leadership and Society 4, pp. 100101.

Hughes et al. (2025): L. Hughes, R. K. Mavi, M. Aghajani, K. Fitzpatrick, S. M. Gunaratnege, S. A. H. Shekarabi, R. Hughes, A. Khanfar, A. Khatavakhotan, N. K. Mavi, K. Li, M. Mahmoud, T. Malik, S. Mutasa, F. Nafar, R. Yates, R. Alahmad, I. Jeon, and Y. K. Dwivedi

Impact of artificial intelligence on project management (PM): Multi-expert perspectives on advancing knowledge and driving innovation toward PM2030.

Journal of Innovation & Knowledge 10 (5), pp. 100772.

Maggoo et al. (2025): L. Maggoo, C. De Pommerol, A. Ardeshna, E. Amiot, L. Bensoussan, J. Haddad, L. Kouyoumji, I. Palencia, and B. Tubiana

Next Frontier in Transforming Telecom: Practical Benefits of Agentic AI and Digital Agents., Technical report, Oliver Wyman.

AI systems are also used to conduct market analyses, synthesize competitive information, and formulate product goals and visions (Lechner et al., 2025; Dell’Acqua et al., 2023).

Lechner et al. (2025): C. Lechner, N. Lang, S. Handschuh, O. Bouffault, and J. Cooper

Can GenAI do your next strategy task? Not yet., California Management Review.

Dell’Acqua et al. (2023): F. Dell’Acqua, E. McFowland, E. R. Mollick, H. Lifshitz-Assaf, K. Kellogg, S. Rajendran, L. Krayer, F. Candelon, and K. R. Lakhani

Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality.

SSRN Electronic Journal

Cost estimation and story point approximation have likewise been explored as application areas (Wagner and Wagner, 2024).

Wagner and Wagner (2024): P. Wagner and R. Wagner

The Evolution of Technology in Artificial Intelligence and Its Impact on Project Management.

In Innovative Methods in Computer Science and Computational Applications in the Era of Industry 5.0, D. J. Hemanth, U. Kose, B. Patrut, and M. Ersoy (Eds.),

Cham, pp. 268–293.

Empirical evidence suggests considerable productivity gains during project initiation and planning, including faster setup of new projects and improved alignment between technical teams and management (Aramali et al., 2025; Brynjolfsson et al., 2023; Hughes et al., 2025).

Aramali et al. (2025): V. Aramali, N. Cho, F. Pande, M. K. S. Al-Mhdawi, U. Ojiako, and A. Qazi

Generative AI in project management: Impacts on corporate values, employee perceptions, and organizational practices.

Project Leadership and Society 6, pp. 100191.

Brynjolfsson et al. (2023): E. Brynjolfsson, D. Li, and L. R. Raymond

Generative AI at work.

Impact of artificial intelligence on project management (PM): Multi-expert perspectives on advancing knowledge and driving innovation toward PM2030.

Journal of Innovation & Knowledge 10 (5), pp. 100772.

GenAI is typically deployed via structured prompts and increasingly integrated into project management tools such as Jira, Confluence, or Microsoft Project (Barcaui and Monat, 2023; Ng et al., 2024). These capabilities contribute to early identification of risks and structured project preparation.

Barcaui and Monat (2023): A. Barcaui and A. Monat

Who is better in project planning?Generative artificial intelligence or project managers?., Project Leadership and Society 4, pp. 100101.

Ng et al. (2024): K. K. Ng, L. Fauzi, L. Leow, and J. Ng

Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development.

arXiv. Note: arXiv:2409.17434 [cs]

Requirements Analysis.

During requirements analysis, Generative Artificial Intelligence assists in drafting, refining, and validating functional and technical requirements.

LLMs are used to generate and elaborate user stories and use cases based on high-level project documentation (Cico et al., 2023; Pinto et al., 2023; Wei et al., 2025) and to create preliminary architectural drafts and structured feature specifications (Glever et al., 2024; Gong et al., 2025; Nitin, 2024).

Glever et al. (2024): P. Glever, A. Balssa, S. Girard, T. Hermann, S. Baccou, and L. Neville

Redefining software engineering: The impact of generative AI.

Technical report Capgemini.

Gong et al. (2025): J. Gong, V. Voskanyan, P. Brookes, F. Wu, W. Jie, J. Xu, R. Giavrimis, M. Basios, L. Kanthan, and Z. Wang

Language Models for Code Optimization: Survey, Challenges and Future Directions.

arXiv. Note: arXiv:2501.01277 [cs]

Nitin (2024): V. Nitin

Using AI to Automate the Modernization of Legacy Software Applications.

In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering,

ASE ’24, New York, NY, USA, pp. 2514–2517.

AI-assisted approaches reduce conceptualization time and accelerate the creation of prototypes, including visually oriented UI/UX artifacts (Wei, 2024; Wei et al., 2025; Gnanasambandam et al., 2025).

Gnanasambandam et al. (2025)

C. Gnanasambandam, M. Harrysson, and R. Singh

How an AI-enabled software product development life cycle will fuel innovation., Technical report McKinsey.

Tools such as Figma AI and related plugins seem promising implementations for rapid prototyping (Petridis et al., 2024).

Petridis et al. (2024): S. Petridis, M. Terry, and C. J. Cai

PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers’ Workflows.

In Proceedings of the 2024 ACM Designing Interactive Systems Conference,

DIS ’24, New York, NY, USA, pp. 743–756.

Furthermore, AI systems can analyze requirement documents to identify inconsistencies, ambiguities, and missing elements, enabling earlier error detection and reducing downstream rework (Gong et al., 2025; Porter et al., 2025).

Gong et al. (2025): J. Gong, V. Voskanyan, P. Brookes, F. Wu, W. Jie, J. Xu, R. Giavrimis, M. Basios, L. Kanthan, and Z. Wang

Language Models for Code Optimization: Survey, Challenges and Future Directions.

arXiv. Note: arXiv:2501.01277 [cs]

Porter et al. (2025)

D. Porter, J. F. DeFranco, and P. Laplante

Requirements Specification Automated Quality Analysis: Past, Present, and Future.

Computer 58 (1), pp. 101–104 (en).

Improved consistency between business and IT stakeholders has also been reported (Pinto et al., 2023; Brynjolfsson et al., 2023).

Pinto et al. (2023): G. Pinto, C. d. Souza, T. Rocha, I. Steinmacher, A. d. Souza, and E. Monteiro

Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes.

arXiv. Note: arXiv:2311.18452 [cs]

Brynjolfsson et al. (2023): E. Brynjolfsson, D. Li, and L. R. Raymond

Generative AI at work.

Design and Implementation.

The design and implementation phase represents the most extensively studied application domain for GenAI. AI-assisted coding tools such as GitHub Copilot provide real-time code completion, refactoring suggestions, boilerplate generation, and context-aware optimization (Peng et al., 2023; Yetiştiren et al., 2023; Solohubov et al., 2023; Glever et al., 2024; Gerdemann et al., 2024).

Peng et al. (2023): S. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer

The Impact of AI on Developer Productivity: Evidence from GitHub Copilot.

arXiv., Note: arXiv:2302.06590 [cs]

Yetiştiren et al. (2023): B. Yetiştiren, I. Özsoy, M. Ayerdem, and E. Tüzün

Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT.

arXiv. Note: arXiv:2304.10778 [cs]

Solohubov et al. (2023): I. Solohubov, A. Moroz, M. Y. Tiahunova, H. H. Kyrychek, and S. Skrupsky

Accelerating software development with AI: exploring the impact of ChatGPT and GitHub Copilot.

Glever et al. (2024): P. Glever, A. Balssa, S. Girard, T. Hermann, S. Baccou, and L. Neville

Redefining software engineering: The impact of generative AI.

Technical report Capgemini.

Gerdemann et al. (2024): D. Gerdemann, S. Garrels, S. Glusac, and K. Bykhanov

Why software + generative AI could take value creation to a whole new level | Kearney.

They also support automatic documentation generation, including code comments and API documentation (Atif et al., 2025; Pinto et al., 2023; Ndiaye et al., 2025), as well as recommendations to improve code structure and maintainability (Gong et al., 2025; Zhong et al., 2025).

Atif et al. (2025): M. Atif, K. Chopra, O. Kilic, T. Wang, Z. Dong, C. Leggett, M. Lin, P. Calafiura, and S. Habib

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics.

arXiv. Note: arXiv:2508.16713 [cs]

Pinto et al. (2023): G. Pinto, C. d. Souza, T. Rocha, I. Steinmacher, A. d. Souza, and E. Monteiro

Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes.

arXiv. Note: arXiv:2311.18452 [cs]

Ndiaye et al. (2025): A. Ndiaye, X. Taraj, B. Robeznik, G. Herzog, C. Gerber, and H. Purwins

Generative AI in Software Engineering: Transforming the Software Development Process.

Technical report, Accenture & German Research Center for Artificial Intelligence.

Gong et al. (2025): J. Gong, V. Voskanyan, P. Brookes, F. Wu, W. Jie, J. Xu, R. Giavrimis, M. Basios, L. Kanthan, and Z. Wang

Language Models for Code Optimization: Survey, Challenges and Future Directions.

arXiv. Note: arXiv:2501.01277 [cs]

Zhong et al. (2025)

M. Zhong, X. Sun, F. Lv, L. Wang, H. Geng, L. Qiu, H. Cui, and X. Feng

BePilot: An AI Programming Assistant for Compiler Backend Development.

ACM Trans. Softw. Eng. Methodol.. Note: Just Accepted

Experimental and field studies consistently report increased development speed (Peng et al., 2023; Cui et al., 2024; Shihab et al., 2025; Struever et al., 2025; Deniz et al., 2023).

Peng et al. (2023): S. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer

The Impact of AI on Developer Productivity: Evidence from GitHub Copilot.

arXiv., Note: arXiv:2302.06590 [cs]

Cui et al. (2024): K. Z. Cui, M. Demirer, S. Jaffe, L. Musolff, S. Peng, and T. Salz

The Productivity Effects of Generative AI: Evidence from a Field Experiment with GitHub Copilot.

An MIT Exploration of Generative AI (en).

Shihab et al. (2025): M. I. H. Shihab, C. Hundhausen, A. Tariq, S. Haque, Y. Qiao, and B. W. Mulanda

The Effects of GitHub Copilot on Computing Students’ Programming Effectiveness, Efficiency, and Processes in Brownfield Coding Tasks.

In Proceedings of the 2025 ACM Conference on International Computing Education Research V.1,

Charlottesville USA, pp. 407–420 (en).

Struever et al. (2025): M. Struever, M. Kropp, and J. Bedard

From Dev Speed to Business Impact: The Case for AI-Assisted Coding and Generative Engineering.

Deniz et al. (2023): B. K. Deniz, C. Gnanasambandam, M. Harrysson, A. Hussin, and S. Srivastava

Unleash developer productivity with generative AI., Technical report, McKinsey.

Routine tasks are particularly affected, with automation reducing manual effort for standardized coding activities (Solohubov et al., 2023).

Several analyses also indicate improvements in review efficiency and defect reduction, although human oversight remains essential (Collante et al., 2025; Yetiştiren et al., 2023; Ndiaye et al., 2025).

Implementation typically occurs via integration into Integrated Development Environments such as Visual Studio Code or IntelliJ, often combined with secure APIs or isolated models to address data protection and compliance requirements (Ng et al., 2024; Ebert and Louridas, 2023).

Testing and Integration.

In the testing and integration phase, Generative Artificial Intelligence is used to automatically generate unit, integration, and end-to-end test cases (Atif et al., 2025; Glever et al., 2024; Maggoo et al., 2025; Zhong et al., 2025).

Atif et al. (2025): M. Atif, K. Chopra, O. Kilic, T. Wang, Z. Dong, C. Leggett, M. Lin, P. Calafiura, and S. Habib

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics.

arXiv. Note: arXiv:2508.16713 [cs]

Glever et al. (2024): P. Glever, A. Balssa, S. Girard, T. Hermann, S. Baccou, and L. Neville

Redefining software engineering: The impact of generative AI.

Technical report Capgemini.

Maggoo et al. (2025): L. Maggoo, C. De Pommerol, A. Ardeshna, E. Amiot, L. Bensoussan, J. Haddad, L. Kouyoumji, I. Palencia, and B. Tubiana

Next Frontier in Transforming Telecom: Practical Benefits of Agentic AI and Digital Agents., Technical report, Oliver Wyman.

Zhong et al. (2025)

M. Zhong, X. Sun, F. Lv, L. Wang, H. Geng, L. Qiu, H. Cui, and X. Feng

BePilot: An AI Programming Assistant for Compiler Backend Development.

ACM Trans. Softw. Eng. Methodol.. Note: Just Accepted

Additional applications include the creation of mock objects and synthetic test data (Yetiştiren et al., 2023; Gong et al., 2025), suggestions for missing test paths and coverage gaps (Cico et al., 2023; Nitin, 2024), and AI-assisted security analysis (Ding et al., 2024; Pearce et al., 2025; Ndiaye et al., 2025).

Cico et al. (2023): O. Cico, B. Cico, and A. Cico

AI-assisted Software Engineering: a tertiary study.

In 2023 12th Mediterranean Conference on Embedded Computing (MECO), pp. 1–6.

Note: ISSN: 2637-9511

Nitin (2024): V. Nitin

Using AI to Automate the Modernization of Legacy Software Applications.

In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering,

ASE ’24, New York, NY, USA, pp. 2514–2517.

-------------------

Ding et al. (2024)

A. Ding, G. Li, X. Yi, X. Lin, J. Li, and C. Zhang

Generative AI for Software Security Analysis: Fundamentals, Applications, and Challenges.

IEEE Software 41 (6), pp. 46–54.

Pearce et al. (2025): H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri

Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions.

Commun. ACM 68 (2), pp. 96–105.

Ndiaye et al. (2025): A. Ndiaye, X. Taraj, B. Robeznik, G. Herzog, C. Gerber, and H. Purwins

Generative AI in Software Engineering: Transforming the Software Development Process.

Technical report, Accenture & German Research Center for Artificial Intelligence.

The literature documents reductions in manual testing effort for routine scenarios and broader, faster test coverage (Kathiresan, 2024; Solohubov et al., 2023; Zhong et al., 2025) as well as earlier detection of edge cases and integration issues (Yetiştiren et al., 2023; Kathiresan, 2024).

Kathiresan (2024): G. Kathiresan

Automated Test Case Generation with AI: A Novel Framework for Improving Software Quality and Coverage.

World Journal of Advanced Research and Reviews 23 (2), pp. 2880–2889 (en).

Note: Last Modified: 2025-03-24T17:45+05:30

Solohubov et al. (2023): I. Solohubov, A. Moroz, M. Y. Tiahunova, H. H. Kyrychek, and S. Skrupsky

Accelerating software development with AI: exploring the impact of ChatGPT and GitHub Copilot.

Zhong et al. (2025)

M. Zhong, X. Sun, F. Lv, L. Wang, H. Geng, L. Qiu, H. Cui, and X. Feng

BePilot: An AI Programming Assistant for Compiler Backend Development.

ACM Trans. Softw. Eng. Methodol.. Note: Just Accepted

--------------------

Yetiştiren et al. (2023): B. Yetiştiren, I. Özsoy, M. Ayerdem, and E. Tüzün

Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT.

arXiv. Note: arXiv:2304.10778 [cs]

Kathiresan (2024): G. Kathiresan

Automated Test Case Generation with AI: A Novel Framework for Improving Software Quality and Coverage.

World Journal of Advanced Research and Reviews 23 (2), pp. 2880–2889 (en).

Note: Last Modified: 2025-03-24T17:45+05:30

In practice, Generative Artificial Intelligence is often integrated into CI/CD pipelines and connected to test frameworks or embedded directly into development environments for automated code analysis and test case generation (Ng et al., 2024; Pinto et al., 2023).

Ng et al. (2024): K. K. Ng, L. Fauzi, L. Leow, and J. Ng

Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development.

arXiv. Note: arXiv:2409.17434 [cs]

Pinto et al. (2023): G. Pinto, C. d. Souza, T. Rocha, I. Steinmacher, A. d. Souza, and E. Monteiro

Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes.

arXiv. Note: arXiv:2311.18452 [cs]

Operation and Maintenance.

In the operation and maintenance phase, Generative Artificial Intelligence supports the analysis of logs, error messages, and stack traces, providing diagnostic insights and suggesting potential remediation steps based on learned patterns (Ng et al., 2024; Gong et al., 2025; Zhong et al., 2025).

Ng et al. (2024): K. K. Ng, L. Fauzi, L. Leow, and J. Ng

Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development.

arXiv. Note: arXiv:2409.17434 [cs]

Gong et al. (2025): J. Gong, V. Voskanyan, P. Brookes, F. Wu, W. Jie, J. Xu, R. Giavrimis, M. Basios, L. Kanthan, and Z. Wang

Language Models for Code Optimization: Survey, Challenges and Future Directions.

arXiv. Note: arXiv:2501.01277 [cs]

Zhong et al. (2025)

M. Zhong, X. Sun, F. Lv, L. Wang, H. Geng, L. Qiu, H. Cui, and X. Feng

BePilot: An AI Programming Assistant for Compiler Backend Development.

ACM Trans. Softw. Eng. Methodol.. Note: Just Accepted

It is also used to explain, summarize, and restructure poorly documented or complex legacy code, improving maintainability and knowledge transfer (Nitin, 2024; Atif et al., 2025; Maggoo et al., 2025).

Nitin (2024): V. Nitin

Using AI to Automate the Modernization of Legacy Software Applications.

In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering,

ASE ’24, New York, NY, USA, pp. 2514–2517.

Atif et al. (2025): M. Atif, K. Chopra, O. Kilic, T. Wang, Z. Dong, C. Leggett, M. Lin, P. Calafiura, and S. Habib

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics.

arXiv. Note: arXiv:2508.16713 [cs]

Maggoo et al. (2025): L. Maggoo, C. De Pommerol, A. Ardeshna, E. Amiot, L. Bensoussan, J. Haddad, L. Kouyoumji, I. Palencia, and B. Tubiana

Next Frontier in Transforming Telecom: Practical Benefits of Agentic AI and Digital Agents., Technical report, Oliver Wyman.

Reported effects include faster issue detection and resolution, improved traceability, and enhanced resilience to staff turnover due to automated documentation and knowledge preservation (Ng et al., 2024; Pinto et al., 2023; Nitin, 2024).

Ng et al. (2024): K. K. Ng, L. Fauzi, L. Leow, and J. Ng

Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development.

arXiv. Note: arXiv:2409.17434 [cs]

Pinto et al. (2023): G. Pinto, C. d. Souza, T. Rocha, I. Steinmacher, A. d. Souza, and E. Monteiro

Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes.

arXiv. Note: arXiv:2311.18452 [cs]

Nitin (2024): V. Nitin

Using AI to Automate the Modernization of Legacy Software Applications.

In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering,

ASE ’24, New York, NY, USA, pp. 2514–2517.

Integration commonly occurs within incident and log management platforms such as Jira, ServiceNow, or Grafana, and in sensitive environments through locally deployed or access-controlled AI systems to mitigate compliance risks (Ng et al., 2024; Ebert and Louridas, 2023).

Ng et al. (2024): K. K. Ng, L. Fauzi, L. Leow, and J. Ng

Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development.

arXiv. Note: arXiv:2409.17434 [cs]

Ebert and Louridas (2023): C. Ebert and P. Louridas

Generative AI for Software Practitioners.

IEEE Software 40 (4), pp. 30–38 (en).

GenAI in Agile Software Development

Generative Artificial Intelligence influences the dynamics of agile development practices beyond productivity gains.

The literature reports high perceived usefulness and satisfaction in agile teams when using AI-assisted tools (Geyer et al., 2025) and accelerated task completion, leading to shorter iteration cycles and more frequent releases (Zhang et al., 2024; Ulfsnes et al., 2024; Bahi et al., 2024).

Geyer et al. (2025): W. Geyer, J. He, D. Sarkar, M. Brachman, C. Hammond, J. Heins, Z. Ashktorab, C. Rosemberg, and C. Hill

A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development.

arXiv. Note: arXiv:2505.07664 [cs]

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Zhang et al. (2024): S. Zhang, Z. Xing, R. Guo, F. Xu, L. Chen, Z. Zhang, X. Zhang, Z. Feng, and Z. Zhuang

Empowering Agile-Based Generative Software Development through Human-AI Teamwork.

arXiv. Note: arXiv:2407.15568 [cs]

Ulfsnes et al. (2024): R. Ulfsnes, N. B. Moe, V. Stray, and M. Skarpen

Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflow.

arXiv. Note: arXiv:2405.01543 [cs]

Bahi et al. (2024): A. Bahi, J. Gharib, and Y. Gahi

Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges.

International Journal of Advanced Computer Science and Applications 15 (3) (en).

By automating routine coding, documentation, and testing tasks, Generative Artificial Intelligence enables teams to focus on value-generating increments, reinforcing core agile principles such as rapid feedback and continuous delivery. At the same time, role expectations shift:

Product owners, developers, and scrum masters increasingly engage in strategic, creative, and evaluative activities, while routine tasks are partially delegated to AI systems (Diebold, 2025; Bahi et al., 2024; Nasir and Hussain, 2024).

Diebold (2025): P. Diebold

From Backlogs to Bots: Generative AI’s Impact on Agile Role Evolution.

J. Softw. Evol. Process 37 (1).

Bahi et al. (2024): A. Bahi, J. Gharib, and Y. Gahi

Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges.

International Journal of Advanced Computer Science and Applications 15 (3) (en).

Nasir and Hussain (2024): B. Nasir and I. Hussain

Automating Agile Workflows: The Role of Multi-Agent LLMs in Modern Software Engineering.

Technical report

The literature recommends adapting role profiles and strengthening AI-related competencies, particularly critical evaluation skills in AI-supported environments.

Risks and Roadblocks in the Adoption of Generative Artificial Intelligence

Despite substantial productivity gains, the literature identifies a range of short- and long-term risks associated with the integration of Generative Artificial Intelligence into software development. In the short term, concerns relate to reliability, quality control, and security. AI-generated code suggestions are frequently erroneous, with manual correction efforts averaging approximately ten minutes per identified defect (Yetiştiren et al., 2023).

A significant proportion of outputs requires post-editing before productive use (Ziegler et al., 2022). Developers often accept AI-generated snippets with limited scrutiny, suggesting superficial quality control practices (Ziegler et al., 2022; Chen et al., 2021).

Perceived productivity improvements do not always align with objective activity metrics, raising the risk of overestimating efficiency gains (Stray et al., 2025).

Security vulnerabilities represent a further concern: coding assistants can be susceptible to prompt injection attacks, potentially leading to data contamination, unintended code execution, or data leakage (Liu et al., 2025; Cotroneo et al., 2024; Norton et al., 2025).

Long-term risks are more structural. Continuous reliance on AI-generated suggestions may erode deep technical knowledge and increase dependency on AI systems (Ng et al., 2024; Brynjolfsson et al., 2023; Ding et al., 2025).

Automatically generated code can introduce opaque dependencies and architectural inconsistencies, contributing to technical debt (Ebert and Louridas, 2023; Atif et al., 2025; Anderson et al., 2025; Moreschini et al., 2026).

Even syntactically correct code may contain subtle semantic or security-relevant weaknesses (Yetiştiren et al., 2023; Atif et al., 2025).

Organizational dependence on specific vendors poses additional strategic risks, including technology lock-in and compliance exposure (Ng et al., 2024).

At the team level, some studies indicate reduced direct human interaction when AI tools mediate communication, code explanation or problem-solving (Ulfsnes et al., 2024; Cabrero-Daniel et al., 2024), entailing risks of knowledge silos and diminished interpersonal trust.

To mitigate these effects, research recommends maintaining structured exchange formats such as code reviews and sprint retrospectives. More broadly, governance demands increase as Generative Artificial Intelligence becomes embedded in production workflows. Issues of quality assurance, accountability, compliance, and traceability become more salient when AI-generated artifacts are incorporated into production systems (Geyer et al., 2025; Zhang et al., 2024; Ulfsnes et al., 2024; Diebold, 2025; Bahi et al., 2024).

The literature emphasizes the need to formalize review obligations, clarify responsibility for AI-assisted outputs, and establish dedicated governance frameworks to prevent quality degradation and unmanaged risk exposure.

Several roadblocks constrain the realization of Generative Artificial Intelligence’s full potential. A large-scale industry survey (Ahlawat et al., 2024) shows that coding accounts for only 10–15 % of total development time and less than 50 % of developers use Generative Artificial Intelligence. They conclude, that improvements in code generation alone have limited overall impact.

To prevent automation risks from offsetting productivity gains, a human-in-the-loop approach (including quality review, domain judgment, and requirements oversight) is emphasized by industry reports (Deniz et al., 2023; Ahlawat et al., 2024; Struever et al., 2025).

Overall, the literature indicates that Generative Artificial Intelligence exerts measurable influence across all phases of the SDLC, with the strongest effects observed in design, implementation, and testing. However, these gains are neither uniform nor automatically realized—short-term limitations in reliability and security, long-term risks such as skill erosion and technical debt as well as organizational roadblocks may constrain or offset productivity improvements. Effective governance frameworks, structured human-in-the-loop validation, and deliberate organizational adaptation remain critical to translating GenAI’s technical capabilities into lasting benefits.

TPS in Software Develpment - The Seven Wastes in Software Development

Industrial Engineering and TPS in Software Development.

Koichiro Toda

Director - TPS Certificate Institution, Agile&DevOps coach, Scrum Master, Software Engineering Consultant

https://www.linkedin.com/pulse/120-years-industrial-engineering-koichiro-toda-bk8xc/

Toyota production developed on the basis of eliminating seven wastes. Mary and Tom Poppendieck came out with seven wastes in software development to improve the productivity of software development activity.

The seven wastes of software development are:

Partially Done Work
Extra Features
Building the wrong thing - Relearning
Paperwork - Handoffs
Waiting for information - Delays
Task Switching
Defects

Articles on seven wastes in software development:

http://e-technologymanagement.com/tm/index.php?option=com_content&view=article&id=252:the-7-software-development-wastes-part-1&catid=58:lean&Itemid=81

More detailed articles on each waste item

Partially done work

Manifestations of partially done work

Partially done work is the result of batch production. Each person does his work at his own pace from number of assignments given to him. Then the next stage person is given a batch of jobs to choose and complete at his own pace and convenience. Therefore before each and after station or a person, partially done work exists. It is called work in process inventory in manufacturing.

Uncoded Documentation - Requirements/features/flow chartss etc. that are documented for coding.

Unsynchronized Code - Multiple branches of code that live for long periods of time before being merged into the main line.

Untested Code - Code without associated automated tests.

Undocumented Code - .documentation artifacts have to developed.

Undeployed Code - Code waiting for deployment in a "big bang" waterfall way.

Overproduction - Extra features

Extra features
Over production - Jack Milunsky

Building the wrong thing - Rework - Relearning

Relearning
Transportation - Handing off is equal to transportation says Milunsky

Handoffs

Delays

Task switching

Defects

Finding Wastes in Software Development

How to find waste in software development?
http://www.umsl.edu/~htp4b/find_waste.html

Value stream mapping for software delivery

updated 5.11.2026, 7 May 2019

3 June 2012

Productivity Analysis - IT Systems Developers, Developer - Users, and IT Users

IT systems development is occurring at a rapid pace. They form a big segment in both GDP and annual investment of many countries. But the research indicates that the net addition to productivity by the investments in IT is not significant. There are various counter arguments by IT persons that the benefit created by them is not being accounted properly. At the micro level, if productivity analysis is carried out in the organizations of IT Systems developers, Developer-users and IT users and expected productivity and realized productivity are documented, it is definitely possible that their aggregate will be reflected in GDP accounts.

Can data-based science, that is data science be used to do productivity analysis of IT investments in System Developers and Developer - Users. An organization which may be classified as developer can be SAP and Google may be categorized as Developer - User. Are they making more and more profits due to market expansion or there is productivity component in their profits as IT systems are increasing productivity in those organizations. The market may expand, as people are using IT services as a fad and convenience despite the lack of productivity. That is what GDP accounts may be capturing.

PRODUCTIVITY MEASUREMENT

Productivity Measurement of Software Projects

In this industry report (2019), challenges in Productivity Measurement in the software industry are addressed. Learn how ISBSG data can be used to benchmark productivity of completed projects, releases and sprints.

You can download the report.

https://www.isbsg.org/resources/

A comprehensive model for assessing the quality and productivity of the information systems function: toward a theory for information systems assessment

Leon Kappelman

Winter 1997, Information Resource Management Journal

https://www.academia.edu/760088/A_comprehensive_model_for_assessing_the_quality_and_productivity_of_the_information_systems_function_toward_a_theory_for_information_systems_assessment

Industrial Engineering and TPS in Software Development.

Koichiro Toda

Director - TPS Certificate Institution, Agile&DevOps coach, Scrum Master, Software Engineering Consultant

https://www.linkedin.com/pulse/120-years-industrial-engineering-koichiro-toda-bk8xc/

Ud. 5.11.2026, 23.4.2022, 14.2.2022

pub: 28.9.2020

Industrial Engineering in Information Technology (IT) Systems and Organizations

Lesson of Industrial Engineering ONLINE Course.

Industrial Engineering and Information Systems

Larry E.Long
Computers & Industrial Engineering
Volume 2, Issue 1, 1978, Pages 1-6

Abstract
Industrial engineering techniques can increase productivity in computer center operations and user activities by as much as 50–100%.
https://www.sciencedirect.com/science/article/abs/pii/0360835278900025

The lean model of industrial engineering that focused on reducing inventory through solving issues that give rise to delays in job processing was implemented in software development from 1990s.

Industrial Engineering and TPS in Software Development.

Koichiro Toda

Director - TPS Certificate Institution, Agile&DevOps coach, Scrum Master, Software Engineering Consultant

https://www.linkedin.com/pulse/120-years-industrial-engineering-koichiro-toda-bk8xc/

Lean Software Strategies: Proven Techniques for Managers and Developers

Peter Middleton, James Sutton
CRC Press, 06-Mar-2020 (Original 2005 - now eEdition) - Business & Economics - 464 pages

Lean production, which has radically benefited traditional manufacturing, can greatly improve the software industry with similar methods and results. This transformation is possible because the same overarching principles that apply in other industries work equally well in software development. The software industry follows the same industrial concepts of production as those applied in manufacturing; however, the software industry perceives itself as being fundamentally different and has largely ignored what other industries have gained through the application of lean techniques.

https://books.google.co.in/books?id=HFDVDwAAQBAJ

Bibliography

Biblography

Fagan, M.E. 1976. Design and code inspections to reduce errors in program development, IBM Systems J. 15(3): 182-211.

Boehm, B. 1981. Software Engineering Economics, Englewood Cliffs, New York, Prentice-Hall.

Schonberger, R.J. 1986. World Class Manufacturing, New York, The Free Press.

Gilb, T. 1988. Principles of Software Engineering Management, Wokingham, England, Addison-Wesley.

Ohno, T. 1988. Toyota Production System: Beyond Large-Scale Production, Cambridge, MA Productivity Press.

Schulmeyer, G. 1990. Zero Defect Software, New York, McGraw-Hill.

Womack, J.P., Jones D.T., and Ross, D. 1990. The Machine that Changed the World, New York, Rawson Associates.

Paulk, M.C., Curtis, B., Chrissis, M.B., and Weber, C.V. 1993. Capability maturity model, version 1.1, IEEE Software 10(4): 18-27.

Gilb, T. and Graham, D. 1993. Software Inspection, Wokingham, England, Addison-Wesley.

Tierney, J. 1993. Eradicating mistakes from your software process through Poka Yoke, Proc. 6th Inte. Software Quality Week, Software Research Institute, San Francisco, CA, May, pp. 300-307.

Hou, A.C. 1995. Toward Lean Hardware/Software System Development: Evaluation of Selected Complex Electronic System Development Methodologies, Report—Lean 95-01, Lean Aircraft Initiative, Center for Technology, Policy and Industrial Development, Massachusetts Institute of Technology. http://lean.mit.edu/public/index.html.

Sutton, J.M. 1996. Lean software for lean aircraft, 15th Digital Avionics Systems Conference, New York, IEEE, pp. 49-54.

Morgan, T. 1998. Lean Manufacturing Techniques Applied to Software Development, MSc Thesis, Massachusetts Institute of Technology.

Raman, S. 1998. Lean software development: Is it feasible? 17th Digital Avionics Systems Conf., New York, IEEE, pp. 13-18.

Robinson, A.G. and Stern, S. 1998. Corporate Creativity: How Innovation and Improvement Actually Happen, San Francisco, CA, Berrett-Koehler.

Hamilton, T. 1999. A Lean Software Engineering System for the Department of Defense, Massachusetts Institute of Technology, MSc Thesis.

Womack, J.P. and Jones, D.T. 1997. Lean Thinking, London, Touchstone Books.

Womack, J.P. and Jones, D.T. 1994. From lean production to the lean enterprise, Harvard Business Rev. 72(2): 93-103.

ud. 5.11.2026

Pub. 21/7/2020

Information Systems Industrial Engineering - Information Systems Engineering

Lesson 431 of Industrial Engineering ONLINE Course - Applied Industrial Engineering Module

Ubiquity of Industrial Engineering Principle - Industrial Engineering is applicable to all branches of engineering.

In each branch of engineering the following three areas of industrial engineering are to be applied to increase productivity and reduce unit cost of output.

Facilities Industrial Engineering - Product Industrial Engineering - Process Industrial Engineering

Picture Source: https://www.ecpi.edu/blog/what-are-computer-based-information-systems

News Related to Information Systems Industrial Engineering - Information Systems Engineering

2023

Generative AI by 2030 - KPMG article

https://www.communicationstoday.co.in/a-sneak-peek-into-2030-with-generative-ai/

IT Cost Reduction Strategies

https://www.leanix.net/en/wiki/ea/it-cost-reduction-strategies

HOW TO REDUCE SOFTWARE DEVELOPMENT COSTS?

https://www.linkedin.com/pulse/how-reduce-software-development-costs-harry-nguyen/

2022

Monq AIOps platform

World’s first topology-based AI-Ops automation platform

Monq is the world’s only self-hosted freemium platform for automated data collection and analysis. It turns your observability into proactive monitoring, helping IT teams prevent possible outages while minimizing costs and efforts.

https://www.producthunt.com/posts/monq-aiops-platform

https://monqlab.com/

Market Guide for AIOps Platforms

Summary

AIOps platforms enhance decision making across I&O personas by contextualizing large volumes of operational data. I&O leaders should use AIOps platforms to improve analysis and insights across the application life cycle, in addition to augmenting IT service management and automation.

Published: 06 April 2021

https://www.gartner.com/en/documents/4000217/market-guide-for-aiops-platforms

Information Systems Industrial Engineering

Costing Information Services

Raymond P. Lutz, Associate Professor of Industrial Engineering

Bull Med Libr Assoc. 1971 Apr; 59(2): 254–261.

In the medical profession information centers, users can benefit directly by having information searched by medical library professionals and readily available. If the users of an information system are to share in the operating expenses, some equitable system of charges must be established. The alternative systems have been reviewed, alternative methods of establishing prices are presented along with a typical example of what these prices might be, ranging from $7.50 to $2.50 per request. The implementation of the cost system is outlined.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC197574/

Industrial Engineering and Information Systems

Areas of Information Systems Industrial Engineering

Productivity in Computer Center Operations (1978)
Minimizing Memory Time and Processing Time through Algorithms Improvement
Programmer productivity
Computer-aided Software Engineering Tools
Software Cost Estimation and Reduction
Lean Software Development
Manpower Planning for Software Development
Human Computer Interaction - Fatigue, Comfort and Health Aspects
DevOps Automation and Productivity
Data Center Industrial Engineering - Energy Consumption Reduction
Internet Processes Productivity Improvement

Programmer Productivity

1978

Program Complexity and Programmer Productivity
E.T. Chen
IEEE Transactions on Software Engineering ( Volume: SE-4 , Issue: 3 , May 1978 )

A set of empirical data showing programmer productivity as a function of program control complexity is also presented. The data reveals a step-function-like contour to programmer productivity with increasing program control complexity.
https://ieeexplore.ieee.org/document/1702520

Some basic determinants of computer programming productivity
Author: Earl Chrysler
Communications of the ACM, Vol. 21, No. 6, June 1978 https://doi.org/10.1145/359511.359523
https://dl.acm.org/doi/abs/10.1145/359511.359523

1979

Impact of valid selection procedures on work-force productivity. (Remember F.W. Taylor)
Schmidt, F. L., Hunter, J. E., McKenzie, R. C., & Muldrow, T. W. (1979). Impact of valid selection procedures on work-force productivity. Journal of Applied Psychology, 64(6), 609–626. https://doi.org/10.1037/0021-9010.64.6.609

Used decision theoretic equations to estimate the impact of the Programmer Aptitude Test (PAT) on productivity if used to select new computer programers for 1 yr in the federal government and the national economy. Based on the data and its interpretation, it is concluded that the impact of valid selection procedures on work-force productivity is considerably greater than most personnel psychologists have believed.

https://psycnet.apa.org/record/1981-02231-001

Human Factors in Software Engineering
December 1979, pp. 6-7, vol. 12
DOI Bookmark: 10.1109/MC.1979.1658569
J.D. Gannon

Software engineering is an effort to reduce the cost of producing software by raising programming productivity and lowering maintenance effort and to increase the benefits of software by extending application areas and improving service.
https://www.computer.org/csdl/magazine/co/1979/12/01658569/13rRUxBa5A8

1992
https://www.andrews.edu/~vyhmeisr/papers/progprod.html

2011
New metric for measuring programmer productivity
Marwa Solla, Ahmed Patel, Christopher Wills
https://www.researchgate.net/publication/241186592_New_metric_for_measuring_programmer_productivity

Study of Task Processes for Improving Programmer Productivity
Doctor of Philosophy Thesis by Damodaram Kamma, Under the Supervision of Prof. Pankaj Jalote
Indraprastha Institute of Information Technology Delhi
March, 2017
https://repository.iiitd.edu.in/jspui/bitstream/handle/123456789/513/PhD1004.pdf

Studying Task Processes for Improving Programmer Productivity
IEEE Transactions on Software Engineering ( Early Access )
Date of Publication: 11 March 2019
https://ieeexplore.ieee.org/document/8664196

What Predicts Software Developers' Productivity?
2019
Emerson Murphy-Hill, Ciera Jaspan, Caitlin Sadowski, David Shepherd, Michael Phillips, Collin Winter, Andrea
Knight, Edward Smith and Matthew Jorde
https://people.engr.ncsu.edu/ermurph3/papers/tse19.pdf
https://ieeexplore.ieee.org/document/8643844

2020

Steve Jobs promised a new age of programmer productivity. With serverless, it's here.
https://cloud.withgoogle.com/build/productivity/serverless-computing-accelerates-programmer-productivity/

Google Cloud’s VP of Apps Prabhakar Raghavan on workplace productivity
https://cloud.withgoogle.com/build/productivity/do-tools-automation-impact-creativity/

Measuring developer productivity in 2020 for data-driven decision makers
By Bill Harding, Programmer/CEO
Published February 25, 2019. Last updated January 29, 2020.
https://www.gitclear.com/measuring_developer_productivity_a_comprehensive_guide_for_the_data_driven

Data Center Productivity and Energy Efficiency

Industrial Engineering in Data Center Design and Processes

2020
WHAT IS DATA CENTER EFFICIENCY?
https://www.colocationamerica.com/data-center-metrics.htm

2017
Data center performance
https://w2.cleardb.net/best-practices-for-monitoring-and-measuring-data-center-performance/

------------------------------------------------------------------------------------------------------------------

Information Systems Engineering

Jeonbuk National University

Department of Industrial Information Systems Engineering

Department of Industrial Information Systems Engineering deals with the use of computer and information technology and systems in industrial systems. Information system industry and consulting use industrial information systems engineering for reasonable design and operation of IT systems in various economic, industrial and social systems.

Industrial Information Systems Engineering applies scientific principles and information technology to product design and manufacture process, and management of sales and distribution process. Industrial Information Systems Engineering innovates and systematize the corporate work process to provide high-quality products and services with low prices at the right time to the customers, draws reasonable decisions by scientific principles, and uses cutting-edge information technology and internet to establish and operate the latest management system that integrates information delivery and work process. . As the industrial system and social system become more high-level and high-tech, the role of Industrial Information Systems Engineering is expanding.

Department of Industrial Information Systems Engineering was established in March 1986, and aims to foster professionals who are creative and competent, with the basic knowledge and hands-on abilities to make industrial system and social system optimization and information-oriented; the four education goals are:

•Cultivate the basic engineering knowledge as industrial information systems engineering student
•Foster engineering student to have the abilities to manage, plan, analyze, and design
•Cultivate engineering hands-on ability that is suitable for digital, information era
•Cultivate ethics as the righteous engineering student

Main Campus
567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, 54896 Republic of Korea +82-(0)63-270-2114
http://www.cbnu.edu/eng/?menuID=297&mode=view&pid=626&did=647

The Johns Hopkins Information Systems Engineering program

Courses

PREREQUISITES
605.101 - Introduction to Python

605.201 - Intro to Programming Using Java

605.202 - Data Structures

605.203 - Discrete Mathematics

605.206 - Introduction to Programming Using Python

If your prior education does not include the prerequisites listed under Admission Requirements, you may still be admitted under provisional status, followed by full admission once you have completed the missing prerequisites. All prerequisite courses are available at Johns Hopkins Engineering. These courses do not count toward the degree or certificate requirements.

FOUNDATION COURSES
Students working toward a master's degree in Information Systems Engineering are required to take the following three foundation courses before taking any other courses.

605.601 - Foundations of Software Engineering

635.601 - Foundations of Information Systems Engineering

695.601 - Foundations of Information Assurance

One or more foundation courses can be waived by the student’s advisor if a student has received an A or B in equivalent graduate courses. In this case, the student may replace the waived foundation courses with the same number of other graduate courses and may take these courses after all remaining foundation course requirements have been satisfied.

COURSES BY TRACK
The tracks offered represent related groups of courses that are relevant for students with interests in the selected areas. Students are required to choose a track to follow and to take at least three courses from the selected track, including at least one 700-level course. The tracks are presented as an aid to students in planning their course selections and are only applicable to students seeking a master’s degree. They do not appear as official designations on a student’s transcript or diploma.

CYBERSECURITY *
635.671 - Data Recovery & Continuing Operations

635.672 - Privacy Engineering

635.673 - Critical Infrastructure

635.676 - Cybersecurity in Information Systems

635.775 - Cyber Operations, Risk, and Compliance

635.776 - Building Information Governance

695.601 - Foundations of Information Assurance

695.611 - Embedded Computer Systems-Vulnerabilities, Intrusions, and Protection Mechanisms

695.614 - Security Engineering

DATA ENGINEERING *
605.635 - Cloud Computing

605.662 - Data Visualization

605.741 - Large-Scale Database Systems

605.744 - Information Retrieval

605.788 - Big Data Processing Using Hadoop

635.631 - Foundations of Data Analytics

635.632 - Foundations of Data Engineering

ENTERPRISE AND WEB COMPUTING *
605.681 - Principles of Enterprise Web Development

605.684 - Agile Development with Ruby on Rails

605.784 - Enterprise Computing with Java

605.785 - Web Services with SOAP and REST: Frameworks, Processes, and Applications

605.786 - Enterprise System Design and Implementation

605.788 - Big Data Processing Using Hadoop

635.682 - Website Development

635.683 - E-Business: Models, Architecture, Technologies, and Infrastructure

HUMAN-COMPUTER INTERACTION
605.662 - Data Visualization

635.661 - Principles of Human Computer Interaction

645.650 - Foundations of Human Systems Engineering

645.651 - Integrating Humans and Technology

INFORMATION MANAGEMENT *
605.641 - Principles of Database Systems

605.643 - Linked Data and the Semantic Web

605.644 - XML Design Paradigms

605.741 - Large-Scale Database Systems

605.744 - Information Retrieval

635.621 - Principles of Decision Support Systems

NETWORK ENGINEERING
605.772 - Network Security Management

635.611 - Principles of Network Engineering

635.711 - Advanced Topics in Network Engineering

For students with appropriate backgrounds, the following courses may be taken toward the network engineering track. Advisor approval and permission of the instructor is required.

605.677 - Internetworking with TCP/IP I

605.678 - Next Generation Mobile Networks with 5G

605.771 - Wired and Wireless Local and Metropolitan Area Networks

605.776 - Fourth Generation Wireless Communications: WiMAX and LTE

605.777 - Internetworking with TCP/IP II

605.778 - Voice Over IP

SOFTWARE ENGINEERING
605.601 - Foundations of Software Engineering

605.602 - Software Analysis and Design

605.604 - Object-Oriented Programming with C++

605.607 - Agile Software Development Methods

605.608 - Software Project Management

605.609 - DevOps Software Development

605.701 - Software Systems Engineering

605.704 - Object-Oriented Analysis and Design

605.705 - Software Safety

605.708 - Tools and Techniques of Software Project Management

SYSTEMS ENGINEERING
595.660 - Planning and Managing Projects

635.601 - Foundations of Information Systems Engineering

635.792 - Management of Innovation

645.650 - Foundations of Human Systems Engineering

645.662 - Introduction to Systems Engineering

645.667 - Management of Systems Projects

645.742 - Management of Complex Systems

645.753 - Enterprise Systems Engineering

645.754 - Social and Organizational Factors in Human Systems Engineering

645.757 - Foundations of Modeling and Simulation in Systems Engineering

645.761 - Systems Architecting

645.767 - System Conceptual Design

INDEPENDENT STUDY AND SPECIAL TOPICS
635.795 - Information Systems Engineering Capstone Project

635.801 - Independent Study in Information Systems Engineering I

635.802 - Independent Study in Information Systems and Technology II

*This track requires Data Structures.
https://ep.jhu.edu/programs-and-courses/programs/information-systems-engineering#quickset-program_pages_content_4

605.601 - FOUNDATIONS OF SOFTWARE ENGINEERING

Fundamental software engineering techniques and methodologies commonly used during software development are studied. Topics include various life cycle models, project planning and estimation, requirements analysis, program design, construction, testing, maintenance and implementation, software measurement, and software quality. Emphasized are structured and object-oriented analysis and design techniques, use of process and data models, modular principles of software design, and a systematic approach to testing and debugging. The importance of problem specification, programming style, periodic reviews, documentation, thorough testing, and ease of maintenance are covered.

https://apps.ep.jhu.edu/course-homepages?course_number=605.601

635.601 - Foundations of Information Systems Engineering

Creating and operating large-scale information systems requires a holistic approach that manages the blending of software, hardware, networks, and security inherent in modern systems. This course introduces key elements and processes required for designing, analyzing, developing, and integrating complex information systems. The course focuses on the systems engineering approach with specific emphasis on design, development, and deployment. Topics covered include requirements engineering, architecture development, security engineering, cost-benefit analysis, information and networking technologies, and operations.

https://apps.ep.jhu.edu/course-homepages?course_number=635.601

Technion - Information Systems Engineering

This program is administered jointly by the Industrial Engineering and Management and Computer Science faculties. It is an admissions track requiring application at the time of registration to the Technion.

Industrial Engineering and Management:
Professor Lavi Aharon | 04-8294410 | ronlavi at technion.ac.il
Saado Nili | 04-8294405 | nili at cc.technion.ac.il | web.iem.technion.ac.il
Computer Science:
Shamir Hava & Assa Dorit | 04-8294344/4316 | ug at cs.technion.ac.il

https://admissions.technion.ac.il/en/study-tracks/information-systems-engineering/

Blog Posts on Information Systems Industrial Engineering

#IT #MIS #productivity #IndustrialEngineering

Information Systems Industrial Engineering - Information Systems Engineering

https://nraoiekc.blogspot.com/2020/07/information-systems-industrial.html

Industrial Engineering - Information Technology Systems - Productivity Improvement and Cost Reduction Processes

https://nraoiekc.blogspot.com/2013/01/information-technology-systems-cost.html

Industrial Engineering in Information Technology (IT) Systems and Organizations

https://nraoiekc.blogspot.com/2020/07/industrial-engineering-in-information.html

News - Information for Information Generation & Transmission - Operation Analysis

Productivity Improvement of Information Systems through Systems Level IE Studies and Operation Level IE Studies

https://nraoiekc.blogspot.com/2020/07/news-information-for-information.html

$2 Trillion Waste In IT Systems? What are we doing?

https://nraoiekc.blogspot.com/2012/12/2-trillion-waste-in-it-systems-what-are.html

Lean Software Development and IT Enabled Services

https://nraoiekc.blogspot.com/2012/04/lean-software-development-and-it.html

Programming Efficiency - Bibliography

https://nraoiekc.blogspot.com/2012/04/programming-efficiency-bibliography.html

Information Systems Industrial Engineering - IEPM 2020 Batch - NITIE

https://nraoiekc.blogspot.com/2020/08/information-systems-industrial.html

Data Center - Data Analytics and Management - Digital Transformation - Bibliography.

https://nraoiekc.blogspot.com/2020/09/unilever-data-centers-data-analytics.html

Productivity Analysis - IT Systems Developers, Developer - Users, and IT Users

https://nraoiekc.blogspot.com/2020/09/productivity-analysis-it-systems.html

Performance Evaluation of Computer Systems

https://nraoiekc.blogspot.com/2020/12/performance-evaluation-of-computer.html

Kubernetes - Cloud Computing - Cost Reduction and Cost Control

https://nraoiekc.blogspot.com/2021/02/kubernetes-cloud-computing-cost.html

Software Process Efficiency - Bibliography

https://nraoiekc.blogspot.com/2012/06/software-process-efficiency.html

API Based Services for Performance Improvement and Productivity Improvement

https://nraoiekc.blogspot.com/2021/06/api-based-services-for-performance.html

Is There a Cost Reduction Benefit in Using Hybrid Computers?

https://nraoiekc.blogspot.com/2021/06/is-there-cost-reduction-benefit-in.html

Document Databases - Alternative to Tabular, Relational Databases for Productivity Improvement

https://nraoiekc.blogspot.com/2021/08/document-databases-alternative-to.html

Google Engineering Productivity Department - Activities and Accomplishments

https://nraoiekc.blogspot.com/2019/03/google-engineering-productivity.html

Software Engineering - Industrial Engineering in Software Engineering

Is the full capability of the #hardware exploited for value creation? An industrial engineer can evaluate periodically. Software engineering component of industrial engineering.

#IndustrialEngineering #Productivity #CostReduction #InformationSystems #SoftwareEngineering

https://nraoiekc.blogspot.com/2020/07/industrial-engineering-in-data-center.html

Industrial Engineering and TPS in Software Development.

Koichiro Toda

Director - TPS Certificate Institution, Agile&DevOps coach, Scrum Master, Software Engineering Consultant

https://www.linkedin.com/pulse/120-years-industrial-engineering-koichiro-toda-bk8xc/

An industrial engineering approach to software development

D.N. Card, R.A. Berg

Abstract

Many different tools and techniques have been developed to increase software quality and productivity. However, periodic acquisition of improved methods and tools, by itself, does not ensure continual improvement. To be effective, new technology must be integrated into an underlying process. That process must be managed explicitly. This paper describes an industrial engineering approach that treats software development as a process distinct from its unique application to any specific project. Its essential elements include formal process definition, software measurement, process engineering, and quality control. Although already successfully embedded in many manufacturing processes, application of industrial engineering techniques to software remains a novelty. Nevertheless, this approach provides the software enterprise with a long-term plan for improving software quality and productivity.

Journal of Systems and Software

Volume 10, Issue 3, October 1989, Pages 159-168

https://www.sciencedirect.com/science/article/abs/pii/0164121289900290

SWE Industrial Engineering - Software Engineering Process Improvement Function

This guide was written to help organizations establish and sustain a process improvement group as the

focal point of a software engineering process improvement program.

The guide is concerned with the technology used in existing and improved processes and the human side of stimulating a higher quality process.

The term technology is used throughout this guide in the broadest sense. For example, this use of the term would include software inspection as a peer review technology for detecting errors in software development work products; computer-aided software engineering (CASE) as a technology providing automated support for performing activities and creating work products in the software development process; and change management as a technology for planning and implementing organizational change.

How do organizations move from their current state to one where there is continuous improvement? First, they must establish an organizational commitment to quality; next, they must create an entity in the organization that is the focal point, some group responsible for facilitating actions supporting that commitment; and finally, they must carefully plan each step to move from the current situation to the desired one. In the software industry, the organizational focal point is a software engineering process group, and the model for the step-by-step change is the process improvement cycle.

The Process Improvement Cycle

Software process improvement is a continuous cycle.

1. Set expectations based on capabilities of new technologies and benchmarking.

2. Assess the current practice inside the organization.

3. Analyze the variance between expectation and practice.

4. Develop and Propose changes that will reduce the variance and thereby improve the process.

5. Plan the integration of the improvements into the existing process and update the process definition. If a formal process definition does not exist, it should be documented now.

6. Implement the improvements.

7. Perform the process as it is now defined.

8. Start over.

The Process Group - Industrial Engineering Group

The software engineering process group is the focal point for process improvement. Composed of line practitioners who have varied skills, the group is at the center of the collaborative effort of everyone in the organization who is involved with software engineering process improvement. Group size is usually equal to 1-3% of the development staff.

Following are ongoing activities of the process group:

• Obtains and maintains the support of all levels of management.

• Facilitates software process assessments.

• Works with line managers whose projects are affected by changes in software engineering practice, providing a broad perspective of the improvement effort and helping them set expectations.

• Maintains collaborative working relationships with software engineers, especially to obtain, plan for, and install new practices and technologies.

• Arranges for any training or continuing education related to process improvements.

• Tracks, monitors, and reports on the status of particular improvement efforts.

• Facilitates the creation and maintenance of process definitions, in collaboration with managers and engineering staff.

• Maintains a process database.

• Provides process consultation to development projects and management.

The process group is not part of product development but is staffed by practitioners. As a result, it has expertise in software engineering.

An improved process also allows easier acquisition and adoption of new technology because that technology can be acquired in direct support of defined processes. The process definition necessary to a disciplined software process is also prerequisite to reasoned analysis about what software tools and methods best support the goals and the creation of products and systems within the organization.

Improvement Activity Plan Implementation Steps

1. Overview

•Goals and objectives

•Related policies

•Needs analysis

2. Technology description

3. Enabling technology

description

4. Sources for technology

and related services

5. Purchases

6. Tailoring

7. Education and training

8. Technology selection

procedure

9. Evaluation procedures

10. Schedule and responsibilities

A process that is not well understood and articulated cannot be managed or improved. Just as manufacturers must select activities and tools for a particular product line, software organizations must also define and implement appropriate processes for each major development effort. Software, by its nature, is very easy to change. Because software practitioners are well-educated professionals who readily take independent action, a well articulated and understood process is essential to the orderly and predictable operation of a software development organization.

A well-defined and articulated description is prerequisite to process improvement. As Card and Berg [Card89] state, "periodic acquisition of improved methods and tools, by itself, does not ensure continual improvement. To be effective, technology must be integrated into an underlying process. That integration must be managed explicitly." Increases in quality and productivity, along with the successful use of technology, depend directly on a well articulated and well-understood process.

Card and Berg [Card89]: An industrial engineering approach to software development

D.N. Card, R.A. Berg, Journal of Systems and Software, Volume 10, Issue 3, October 1989, Pages 159-168 https://www.sciencedirect.com/science/article/abs/pii/0164121289900290

Describing the Existing Process

Building a process definition for an organization begins with describing what exists. Eventually, the definition of what should exist is also created, and replaces the initial description.

Recommended reading for those beginning the task of describing or defining software process:

[Humphrey89], Ch. 13, "Defining the Software Process."

Any of a number of different methods may be used; one of the simplest, ETVX [Radice85], is described below. Another straightforward method with tool support is described in [Kellner89]. Whatever the method, describing the existing process should result in documentation of how software products are actually developed and maintained in a given organization.

4.1.1. Documenting the Process: One Approach

One very accessible approach to writing a process description is presented in [Radice85]. Radice considers a process to be made up of software activities, which must be defined. A defined activity is one which has:

(1) a list of entry criteria that should be satisfied before beginning the tasks, (2) a set of task descriptions that indicate what is to be accomplished, (3) a validation procedure to verify the quality of the work items produced by the tasks, and (4) a checklist of exit criteria that should be satisfied before the activity is viewed as complete. (p. 83)

Radice calls this the E (Entry Criteria) T (Tasks) V (Validations) X (Exit Criteria) process architecture. Using ETVX to describe each phase of software activity should result in a consistent description of software development processes. Gaps—missing activities or a missing E, T, V, or X for an activity—that appear in the process description indicate where immediate process improvement may be needed. For example, the exit criteria for module design may not have been defined or may not match the entry criteria for the next activity. On the most abstract level, process definition is simply the determination of what stages and activities will be used by a given software organization.

[Radice85] lists the following phases for a commercial product:

• Requirements and planning

• Product-level design

• Component-level design

• Module-level design

• Code

• Unit test

• Functional verification test

• Product verification test

• System verification test

• Package and release

• Early support program

• General availability

E, T, V, and X must be defined for each activity.

Types of Measurement

In general, there are two types of measures: product and process. Product measures describe the characteristics of each system component being produced or maintained. Typical product measures are size, cost, complexity, quality (number of defects), and resources consumed to develop or maintain the component. Process measures describe aspects of the process used to produce or maintain those components, and are attributable to the human processes that create the product. Typical process measures are defect rates, repair rates, and production rates. Broader measures such as labor turnover, learning curve, communications overhead, and degree of overall adherence to the defined process also relate to process. In fact, a single measure may apply to both process and product, depending upon its usage and one’s point of view. Both types of measures may be collected on individual components or across groups of components; when product measures are aggregated, they often provide information about the process.

Defect Prevention

Several process information files are needed to support the defect prevention process.

• The defect file contains a record of each defect and information such as originator, description, resolution, disposition, and effort to repair.

• The action item file contains the action items that arise out of a causal analysis of the defects. These items are suggestions for improving the process so that future defects would not be manifest in the software product.

The process group should capture the history of the improvement efforts. The group should collect, catalog, and maintain reports and other artifacts related to attempts to improve the process. In many organizations, these products are lost when personnel change, and the improvement lessons must be learned all over again. Records should include: an indication of what went well and what did not go well; a description of how problems were handled; and suggestions for improving performance on similar tasks in the future. Capturing and reviewing lessons learned can be a part of the development life

cycle. The challenge is to make this information available to those who need it, past the life of a project.

Beginning Continuous Improvement

The process of introducing an improvement includes selecting a candidate technology, tailoring the technology and the training for its use, and using the technology on a pilot basis. Feedback should be collected and the full implementation plan is developed in light of the pilot experience.

One key long-term activity is the installation, over and over again, of new procedures and technology that support process improvement. These installations should begin with a pilot (prototype) installation—a controlled experiment. Pilots are essential when an organization has no experience with a technology, or when the technology will be applied in a new domain or with inexperienced staff. This is true even if the technology seems to be mature, as with software inspections, or even if the organization has excellent in-house resources, such as a technical working group with some experience in cost estimation. If a technology is new to an organization, it cannot be considered mature in that context. Appendix D presents an extended discussion of this phenomenon. This chapter describes some considerations for executing a pilot effort.

Process Consultation

The process group spends a significant proportion of its time coaching others and problem solving, based on the examples of best practices developed in other organizations. This consulting and broad awareness of the quality of particular efforts is indispensable for the success of the overall process improvement program.

As the focal point for process improvements, the process group spends a significant proportion of its resources meeting with those whom it serves. The group’s consulting activities include:

• Helping to set realistic expectations for improvement activity results.

• Tailoring process priorities, definitions, standards, training, and other process

materials.

• Suggesting methods for improving a specific process if a working group has not

already done so.

• Analyzing process measurement data.

• Facilitating improvement planning meetings and activities.

• Demonstrating improvement technology. (For example, serving as a moderator

for inspections until a project can develop its own inspection moderators.)

• Referring groups with similar interests to each other so that they can help each

other.

Process group members must have or develop good consulting skills; they need the ability

to listen and clarify, and to collaborate in problem solving with those who seek their help. If

process group members have access to training or expert advice (especially in the form of

"shadow" consulting) in this area, they should take advantage of it.

Process Group Membership

Process group members should collectively have experience from throughout the software life cycle.

Members of the process group are advocates for improvement. They are software professionals assigned, generally full time, to help the organization increase the maturity of its

software process. Members should be carefully selected with the goal of balancing the experience and educational background of the group as a whole.

Selecting the Process Group Leader

The process group leader must be an acknowledged technical leader,

with these characteristics:

• Extensive experience in or knowledge of the software process.

• Experience advocating improved software development processes, methods,

and tools—that is, improved quality and productivity.

• Experience in management or project leadership.

• Knowledge of the software development environment

Robert L. Grady and Deborah L. Caswell, Software Metrics: Establishing a Company-Wide Program, Prentice-Hall, 1987.

• H. J. Harrington, The Improvement Process: How America’s Leading Companies Improve Quality, McGraw-Hill, 1987.

• Watts Humphrey, Managing the Software Process, Addison-Wesley, 1989.

• Rosabeth Moss Kanter, The Change Masters: Innovation for Productivity in the

American Corporation, Simon and Schuster, 1983.

• Marvin Weisbord, Productive Workplaces: Organizing and Managing for Dignity, Meaning, and Community, Jossey-Bass, 1987.

Process Management Approach - Card

The process management approach includes three essential activities: process definition,

process control, and process improvement. An undefined process cannot be controlled. An

uncontrolled process cannot be improved consistently. Because improvement means

change, attempting to improve an unstable process often leads to further instability. Figure

B-3 shows how these elements are connected by the common threads of performance data

and corrective action.

The process definition activity provides a prescription for performing work. Measuring the

initial process performance establishes a baseline against which subsequent performance

can be compared. The process control activity is concerned with identifying and correcting

special causes of poor quality, to keep the process performing as intended. That is, it seeks

to maintain key quality parameters within pre-defined control limits. The process improvement activity seeks to identify and rectify common causes of poor quality by making basic

changes in the underlying process.

Software Design Improvement

Until recently, hardware designers left many important product quality considerations to be handled by manufacturing engineers. Because software does not go through a corresponding manufacturing phase, the software engineer must deal with those producibility concerns directly [Card90]. That is, the software system must be designed to be easy to implement and maintain. This is in addition to satisfying the customer’s functional requirements.

Process Design Improvement

Once the process has been defined and controlled, management can turn its attention to improving the underlying process. This can be done by simplifying the process and by inserting appropriate new technology. [Turner78] suggests five questions that should be asked about each process element:

1. Is this activity necessary or can it be eliminated?

2. Can this activity be combined with another or others?

3. Is this the proper sequence of activities?

4. Can this activity be improved?

5. Is the proper person doing this activity?

Initial process improvement efforts should be concentrated at leverage points: those activities that require the most effort and produce the most problems (see, for example, Pareto

analysis in [Grady87]). Improvement actions should be evaluated in situ by studying their

effect on actual process performance.

An Introduction to Technological Change

This topic addresses the fundamentals of implementing technological change in software organizations. The process group can greatly improve its odds for success if it understands and acquires some skills in managing the technological and organizational change attendant to process improvement.

It is an introduction to the basic knowledge that is prerequisite to effective and predictable technological change in the context of software engineering.

Technology Mapping

Mapping is a simple but powerful way to determine whether a technology is likely to succeed in an organization. The mapping process essentially involves: 1) examining the technology to be implemented for aspects of context that have been built into it and 2) comparing ("mapping") the results of this examination to the results of the organizational context analysis. If there is minimal overlap, the risk is high that implementing the technology will be complex and unpredictable and will require more resources, especially in pilot efforts. If there is a significant overlap, the chances of success are greater. The comparison should take into account the fact that some aspects of context may carry more weight than others. Context analysis is a useful step to take just prior to beginning a search for a technology. It

can help narrow the candidate field by noting major aspects of context, such as application domain, technical compatibility, and budget, which would seriously constrain possible matches. Once the list is pared down to a half dozen or so technologies, mapping is a major step in the final selection process.

Mapping is necessary because all software engineering technologies are based on a set of assumptions. Certain application domains or classes of application domains—for example, real-time embedded, MIS, scientific—may be assumed when a technology is developed. Certain types of users—for example, ordinary citizen, engineer, scientist, bank teller—may also be assumed. Certain technical requirements—machine type and size, operating system, network type—are usually stated. The easiest technologies to eliminate from a candidate list are those with the least overlap with the organization’s context; as the list shrinks, the mapping process requires more detective work and attention to subtleties such as software engineering terminology, life-cycle phase coverage and definition, and style of work in groups. Even a technology that is well targeted to a particular market niche cannot take all variations of an organization in that niche into account. The best test of a new technology is a series of pilot uses, thoroughly evaluated.

When an organization is dealing with newer and softer technologies, mapping becomes critical. If transfer mechanisms are people-based, they are heavily influenced by frame of reference and by the skills of the people who must translate between frames of reference in the

transfer process.

Boundary Spanners

Boundary spanners are people who are comfortable in multiple frames of reference; they

can work effectively as members of a business organization, with management and engineers, and on professional society committees. Examining the process of communicating in

different languages provides a useful analogy to the services boundary spanners perform.

Because the mapping process is similar to translation between languages, it is best done by

boundary sponsors who are the equivalent of multi-lingual. If these people have extensive

experience in translation, they will know when to proceed cautiously; they will understand

the need, for example, to define carefully terms that most people will assume are well understood.

Boundary spanners are common outside organizations in the technology advocate roles described in Figure D-3. These boundary spanners play an important role in getting new products from research into the marketplace, in articulating the new technology in universities and professional communities, in marketing the technology once it becomes a product, and in supporting new users.

In their role, boundary spanners typically serve on technical working groups or as members of the process group "porting" technology experience from one organizational group to another; they

connect the inside of the organization to the outside world. They track technology directly

[Carlyle88]; they may also track it by serving on outside working groups such as the IEEE

Computer Society standards committees. Having largely internalized context analysis

results for their own organization, they effectively scan for and select candidate

20 technologies. People who perform this role best have had experience in a number of

contexts; criteria for boundary spanners are much the same as for members of process

group

See search results of SEI software process improvement

Software Development Processes

https://opendsa-server.cs.vt.edu/ODSA/StandaloneModules/20221129151902/html/IntroProcess.html

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