Research synthesis · Sustainable software engineering

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

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.

A short narrative review of research by Narasimha Rao Vajjhala and co-authors in this area. Every claim carries an APA citation that links to the publication’s own page (abstract, key findings, DOI); the full references are listed at the end. Updated .

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) 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), and an edited volume set out how Internet of Things solutions can be designed to minimise environmental impact across industries (Thandekkattu & Vajjhala, 2024).

On the second, the group has studied software quality and the people who build software. Saheed et al. (2021) 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) 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) 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; Strang & Vajjhala, 2022b), 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).

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).
  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).
  3. Project manager experience, budget and in-house versus outsourced management predict IT project failure (Strang & Vajjhala, 2023a).

The publications and what each contributes

PublicationMethodCore finding or contribution
Strang and Vajjhala (2026a)
Systems
Single-case socio-technical systems study of archival project records (N = 207) using an open-source data science workflow: correlation analysis, principal component analysis, common-method-bias diagnostics, and kNN and SVM classification.In 207 financial software project records, stakeholder-rated social and governance factors tracked the overall project score almost perfectly (r = +0.995 and +0.966) while the environmental factor was unrelated to it, and machine learning classifiers performed weakly (kNN AUC = 0.497; SVM accuracy 61.8%).
Fonkam and Vajjhala (2026)
Computational Science and Computational Intelligence (CSCE 2025)
Systematic literature review of 28 empirical studies (2015–2025) on functional programming and Haskell in education, drawn from ACM DL, IEEE Xplore, Google Scholar and computing-education venues, with thematic synthesis.A systematic review of 28 empirical studies (2015–2025) found mixed but promising outcomes: integrated approaches outperform purely functional ones, benefits for mathematical reasoning and abstract thinking are consistent, and transfer to mainstream object-oriented languages is selective.
Strang and Vajjhala (2026c)
Information
Quantitative instrument development and validation with survey data from 2,231 project sponsors and decision-makers in North American manufacturing (NAICS 31–33): principal component analysis, exploratory factor analysis and structural equation modelling.A survey of 2,231 North American manufacturing project sponsors produced a validated seven-item, two-factor instrument — ESG planning and ESG monitoring and controlling — with excellent fit (CFI = 0.99, TLI = 0.98, RMSEA = 0.052, SRMR < 0.035).
Strang and Vajjhala (2025a)
Information
Post-positivist quantitative hypothesis testing on Fintech project data: correlation, conditioned correlation, t-tests and ordinary least squares regression with budget, end-user community size and certification as controls.Team members’ willingness to disclose their past performance evaluations during procurement significantly improved project success after controlling for budget, end-user community size and certification, roughly doubling the explained variance in project success from 9.6% to 18.8%.
Thandekkattu and Vajjhala (2024)
IGI Global (edited book)
Edited volume of 13 chapters; chapters include technical frameworks (smart farming with AIoT, IoT data fusion for disease surveillance), a meta-analysis of IoT integration in education, and sector reviews (manufacturing, dairy, mobility, SMEs).The volume shows how IoT can be designed for sustainability across education, waste reduction, agriculture and smart farming, disease surveillance, autonomous vehicles, Industry 5.0 manufacturing and SMEs, combining technical frameworks with sector reviews.
Strang and Vajjhala (2023a)
International Journal of Information Technology Project Management
Pragmatic recursive mixed-methods design on U.S. Government Accountability Office big data (about 17,430 IT projects): structured data cleaning, random forest classification of project breach/failure, and post-hoc logistic regression in SPSS.Random forest analysis of about 17,430 US government IT projects identified seven failure indicators with 81% recall and a ROC area of 0.849.
Strang and Vajjhala (2022a)
International Journal of Information Technology Project Management
Online repeated-measures controlled experiment with 16 experienced U.S. IT project managers in a simulated military software project, using a PERT competency test, basic and incentive-biased decision conditions, paired t-test and repeated-measures ANOVA.A 400% bonus incentive significantly lowered 16 experienced IT project managers’ likelihood of cancelling a failing project (from 3.438 to 2.438 on a 1–5 scale; p = .005, Cohen’s d = 0.826).
Strang and Vajjhala (2022b)
The Journal of Modern Project Management
Repeated-measures controlled quasi-experiment with 24 experienced U.S. project managers (competency test, basic decision, biased decision) analysed with Spearman correlation, MANOVA, repeated-measures ANOVA, Friedman test and post-hoc t-tests.In a controlled experiment with 24 experienced US project managers, a biasing incentive significantly lowered decision quality, and certification and risk competency were the only individual factors significantly related to good decisions (rho = 0.549 to 0.712).
Saheed et al. (2021)
Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021, Nashik, India, April 23–24, 2021, Revised Selected Papers, Part I
Experimental machine learning comparison: seven ensemble models (CatBoost, LightGBM, XGBoost and boosted/bagged variants) plus a logistic regression baseline evaluated on six software defect datasets (NASA repository) with AUC, precision, recall, F-measure and Matthews correlation coefficient.A seven-model ensemble of boosted and bagged learners, evaluated with AUC, precision, recall, F-measure and Matthews correlation coefficient on NASA defect datasets, outperformed a logistic regression baseline, and the ensemble CatBoost model gave outstanding performance on all three datasets reported.

Key terms in this area

ESG compliance
The degree to which an organization or project meets Environmental, Social, and Governance criteria set by regulators, investors, or internal policy. — as used in Strang and Vajjhala (2026a)
Socio-technical system
A system in which people, organizational rules, and technology jointly determine outcomes, so that none can be understood in isolation. — as used in Strang and Vajjhala (2026a)
Common method bias
Inflated correlations that arise because several variables were measured by the same source or instrument rather than because the constructs are truly related. — as used in Strang and Vajjhala (2026a)
Software defect prediction (SDP)
Research field in the software development life cycle in which machine learning techniques are used to predict software defects, where accurate prediction would assist software developers and engineers in developing a reliable software product. — as used in Saheed et al. (2021)
Random forest
An ensemble machine learning technique that builds many decision trees and combines their votes to classify cases, here IT projects as failed (breached) or successful. — as used in Strang and Vajjhala (2023a)
Node purity
In random forest, the increase in node purity measures a feature's importance, which the authors liken to the change in r2 effect size when a factor is added to a regression model. — as used in Strang and Vajjhala (2023a)
ROC area (ROCa)
The area under the Receiver Operating Characteristic curve, estimating how well the model's classifications separate the two outcome classes; also called AUC. — as used in Strang and Vajjhala (2023a)

All key terms across the publications →

Standards, datasets and methods named in these studies

References

How to cite this synthesis

Please cite the original publications above for specific findings. To cite this overview itself:

Vajjhala, N. R. (2026, October 4). Sustainable software engineering: ESG in software projects, software quality and reliable IT project delivery. Narasimha Rao Vajjhala. https://www.narasimharao.net/research/focus-areas/sustainable-software-engineering/

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