Author

P. Pareek

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access 2026

Sustainability-Centric Software Development: A Quantitative Framework for the SDLC

Although software systems increasingly shape energy consumption, economic output, and societal welfare, most development methods still prioritise schedule, cost, and functionality. This paper presents an approach to software development that prioritises sustainability by embedding environmental, economic, and social objectives into the SDLC from the very beginning. The framework represents sustainability as a set of quantifiable variables that are combined into a Global Sustainability Index (GSI). These metrics include operational energy and carbon footprint, total cost of ownership (TCO), maintainability index, defect density, and a normalised Social Impact Score (SIS). An empirical measurement architecture gathers runtime and process data for continuous improvement, while phase-level “sustainability budgets” direct trade-offs across requirements, design, implementation, testing, and operation. The framework is evaluated in a repeated-measures industrial study of six production software systems (758 KLOC in total, 58 engineers, six application domains), in which every system is observed over four counterbalanced release cycles governed respectively by the proposed framework and by three established approaches: GREENSOFT, GreenSDLC, and the Sustainability Quality Model (SQM). The proposed framework attains the highest GSI (0.86 ± 0.03), a statistically significant improvement of 10–19% over the competing frameworks (paired t-tests, all Holm-adjusted p < 0.002, Cohen’s dz > 2.5). Relative to current models, energy usage and carbon emissions are cut by 10–20%, and they are decreased by approximately 30% when compared to a no-framework baseline. Normalised maintainability and defect density both improve over a five-year timeframe, and total cost of ownership drops 4–9%. Consistently higher levels of social impact and stakeholder satisfaction are observed, particularly for user groups who are marginalised. Ninety-five percent confidence intervals and effect sizes are reported for every headline comparison, and the principal limitations of the framework are stated explicitly together with mitigation strategies. These results show that all three dimensions can be improved with explicit quantitative sustainability integration without a rise in long-term costs.

M. K., P. Pareek · International Journal of Adv... · 0 citations

Open access Jul 2026

Cross-platform software vulnerability detection using Vulnerascope-X with Word2Vec Node2Vec and RCGO optimized SVM

To increase program dependability and reduce maintenance expenses, software defect prediction is essential. For software vulnerability study across platforms, this paper presents a state-of-the-art methodology that makes use of a unique synthetic dataset called VulneraScope-X. Utilising synthetic CVE intelligence, this dataset incorporates a wealth of static, syntactic, and semantic information. This work employs an array of pre-processing methods, such as min-max normalisation, Word2Vec, Node2Vec, Synthetic Minority Oversampling Technique (SMOTE) for class balance, to deal with complexity and diversity of the features. The recently suggested Running City Game Optimiser (RCGO) outperformed state-of-the-art metaheuristics in feature selection while simultaneously lowering dimensionality and keeping predictive characteristics. With help of the features that were chosen, a Support Vector Machine (SVM) classifier was trained. The hyperparameters of this classifier were adjusted using Grid Search. Outperforming more conventional classifiers like Naive Bayes, MLP, and KNN, the model produced remarkable results with a 94.25% accuracy rate, 93.90% precision rate, 94.10% recall rate, and AUC-ROC of 0.962. In terms of accuracy and execution time, the RCGO algorithm outperformed other optimisation algorithms such as GA, GWO, CRO, and BWO. This scheme provides reproducible and scalable methodologies for software security evaluation in addition to demonstrating a high-performing pipeline for defect prediction. According to the findings, VulneraScope-X greatly improve cross-platform defect detection when combined with topological and semantic embeddings. When applied to large-scale, heterogeneous software organizations, this method demonstrates promise for vulnerability triaging and safe software development.

Vijayamahantesh, A. Ashwitha, E. Naresh et al. · Scientific Reports · 0 citations