# Sustainable software engineering: ESG in software projects, software quality and reliable IT project delivery

Research synthesis · Sustainable software engineering
Canonical page: https://www.narasimharao.net/research/focus-areas/sustainable-software-engineering/
Author: Narasimha Rao Vajjhala, Professor and Chair of Computer Science, American University in Bulgaria (ORCID 0000-0002-8260-2392)
Updated: 2026-09-25

> Vajjhala and colleagues address sustainability in software engineering in two senses — whether software projects can be assessed against ESG criteria, and whether software and IT projects are built to last through defect prediction, sound programming education and evidence on why IT projects fail.

## Synthesis

Narasimha Rao Vajjhala’s work bears on sustainable software engineering in two senses: whether software projects can be assessed against environmental, social and governance criteria, and whether software and the IT projects that deliver it are built to last.

On the first, [Strang and Vajjhala (2026a)](https://www.narasimharao.net/research/machine-learning-esg-compliance-financial-software-projects/) treated one financial firm’s project evaluation practice as a socio-technical system and applied an open-source data science workflow to 207 software engineering project records. Stakeholder-rated social and governance factors tracked the overall project score almost perfectly (r = +0.995 and +0.966), but the environmental factor was unrelated to it, and machine learning classifiers performed weakly (kNN AUC = 0.497; SVM accuracy 61.8%). The authors concluded that ESG measurement in software projects is shaped by human rating practice and organizational templates as much as by algorithms. A companion study showed that project-level ESG compliance can be measured with a validated seven-item instrument covering ESG planning and ESG monitoring and controlling ([Strang & Vajjhala, 2026c](https://www.narasimharao.net/research/esg-compliance-manufacturing-projects-survey-instrument/)), and an edited volume set out how Internet of Things solutions can be designed to minimise environmental impact across industries ([Thandekkattu & Vajjhala, 2024](https://www.narasimharao.net/research/designing-sustainable-iot-solutions-smart-industries/)).

On the second, the group has studied software quality and the people who build software. [Saheed et al. (2021)](https://www.narasimharao.net/research/ensemble-learning-software-defect-prediction/) showed that a boosted and bagged ensemble, led by CatBoost, predicted software defects on NASA datasets with outstanding performance when judged on AUC, F-measure and the Matthews correlation coefficient rather than accuracy alone. [Fonkam and Vajjhala (2026)](https://www.narasimharao.net/research/functional-programming-haskell-software-education-review/) reviewed 28 empirical studies and found that functional programming taught with Haskell brings consistent benefits for mathematical reasoning and abstract thinking, with integrated approaches outperforming purely functional ones and selective transfer to mainstream object-oriented languages.

Finally, sustainable delivery depends on IT projects that do not fail. Mining about 17,430 US government IT projects, [Strang and Vajjhala (2023a)](https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/) identified project manager experience, project budget and outsourced versus in-house project management as significant failure predictors. Controlled experiments showed that a bonus incentive made experienced project managers less likely to cancel a failing military software project, with risk competency as the protective factor ([Strang & Vajjhala, 2022a](https://www.narasimharao.net/research/project-manager-bias-failing-project-termination-experiment/); [Strang & Vajjhala, 2022b](https://www.narasimharao.net/research/project-manager-risk-decision-bias-crisis-experiment/)), and in Fintech projects team members’ willingness to disclose past performance nearly doubled the explained variance in project success, from 9.6% to 18.8% ([Strang & Vajjhala, 2025a](https://www.narasimharao.net/research/project-team-past-performance-disclosure-fintech-project-success/)).

The resulting view of sustainable software engineering joins measurable ESG criteria with software quality, sound programming education and evidence-based project governance.

## Key claims with sources

1. In financial software engineering projects, social and governance ratings tracked the overall project score almost perfectly while the environmental factor was unrelated to it ([Strang & Vajjhala, 2026a](https://www.narasimharao.net/research/machine-learning-esg-compliance-financial-software-projects/)).
2. An ensemble CatBoost model gave outstanding software defect prediction performance when judged on AUC, F-measure and MCC rather than accuracy alone ([Saheed et al., 2021](https://www.narasimharao.net/research/ensemble-learning-software-defect-prediction/)).
3. Project manager experience, budget and in-house versus outsourced management predict IT project failure ([Strang & Vajjhala, 2023a](https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/)).

## References

- Fonkam, M., & Vajjhala, N. R. (2026). A Spotlight on the Role of Functional Programming & Haskell in Computing & Software Development. In H. R. Arabnia (Ed.), Computational Science and Computational Intelligence (CSCE 2025) (pp. 56–63). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-032-22202-2_5 — summary and key findings: https://www.narasimharao.net/research/functional-programming-haskell-software-education-review/
- Saheed, Y. K., Longe, O., Baba, U. A., Rakshit, S., & Vajjhala, N. R. (2021). An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product. In Mayank Singh, Vipin Tyagi, P. K. Gupta, Jan Flusser, Tuncer Ören, V. R. Sonawane (Eds.), Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021, Nashik, India, April 23–24, 2021, Revised Selected Papers, Part I (pp. 317–326). Springer International Publishing. https://doi.org/10.1007/978-3-030-81462-5_29 — summary and key findings: https://www.narasimharao.net/research/ensemble-learning-software-defect-prediction/
- Strang, K. D., & Vajjhala, N. R. (2022a). How Bias Impacted the Project Manager Decision to Not Terminate a Failing Project. International Journal of Information Technology Project Management, 13(1), 1–17. https://doi.org/10.4018/IJITPM.304059 — summary and key findings: https://www.narasimharao.net/research/project-manager-bias-failing-project-termination-experiment/
- Strang, K. D., & Vajjhala, N. R. (2022b). Testing Risk Management Decision Making Competency of Project Managers in a Crisis. The Journal of Modern Project Management, 10(1), 52–71. https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/JMPM02904 — summary and key findings: https://www.narasimharao.net/research/project-manager-risk-decision-bias-crisis-experiment/
- Strang, K. D., & Vajjhala, N. R. (2023a). Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods. International Journal of Information Technology Project Management, 14(1), 1–24. https://doi.org/10.4018/IJITPM.317221 — summary and key findings: https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/
- Strang, K. D., & Vajjhala, N. R. (2025a). Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success?. Information, 16(11), 955. https://doi.org/10.3390/info16110955 — summary and key findings: https://www.narasimharao.net/research/project-team-past-performance-disclosure-fintech-project-success/
- Strang, K. D., & Vajjhala, N. R. (2026a). Machine Learning and Data Science for ESG Compliance Measurement in Financial Software Engineering Projects: A Single-Case Socio-Technical Systems Analysis. Systems, 14(8), 990. https://doi.org/10.3390/systems14080990 — summary and key findings: https://www.narasimharao.net/research/machine-learning-esg-compliance-financial-software-projects/
- Strang, K. D., & Vajjhala, N. R. (2026c). Verifying SDG ESG Compliance in Manufacturing Industry Projects by Surveying Sponsors. Information, 17(4), 311. https://doi.org/10.3390/info17040311 — summary and key findings: https://www.narasimharao.net/research/esg-compliance-manufacturing-projects-survey-instrument/
- Thandekkattu, S. G., & Vajjhala, N. R. (Eds.). (2024). Designing Sustainable Internet of Things Solutions for Smart Industries. IGI Global. https://doi.org/10.4018/979-8-3693-5498-8 — summary and key findings: https://www.narasimharao.net/research/designing-sustainable-iot-solutions-smart-industries/