International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Explainable AI (XAI) for Software Engineering Decision-Making |
|---|---|
| ABSTRACT | Artificial Intelligence (AI) is now integrated into the software engineering process itself, which includes defect prediction, estimation of effort, requirement prioritization, code review automation, test case generation, and optimization of DevOps. It has been demonstrated that machine learning and deep learning models are highly predictive; however, their opaque decision-making limits their applicability in high-stakes and safety-critical software applications. Explainable Artificial Intelligence (XAI) seeks to address this problem by making AI-powered decisions understandable, interpretable, and trustworthy. This paper provides a thorough outline of the implementation of XAI techniques in software engineering decision systems and compares explainability approaches such as SHAP, LIME, attention mechanisms, and rule extraction using real-life software engineering data. The study examines the trade-off between interpretability and model performance, as well as the impact of explanations on developer trust and engineering decision-making. The integration of XAI techniques resulted in a 38% improvement in developer trust scores (p < 0.05) while maintaining statistically equivalent predictive accuracy. The experimental results further indicate that the inclusion of XAI enhances stakeholder confidence and validation without significantly reducing predictive capability. The study proposes a comprehensive XAI adoption framework aligned with software engineering processes and offers practical guidelines for deploying interpretable AI systems in development environments. |
| TITLE | |
| AUTHOR | HARSH VERMA |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311002 |
| pdf/2_Explainable AI (XAI) for Software Engineering Decision-Making.pdf | |
| KEYWORDS | |
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