Systematic Review of Explainable AI Techniques in Software Engineering and Decision-Making

Particularly in domains where accountability, openness, and trust are critical, artificial intelligence (XAI) has become a vital subject of study. Serious worries over the "black-box" nature of these algorithms have been raised by the increasing use of deep learning and machine learning models in software engineering and decision-making. When interpretability is poor, there are risks in automated decision support, software quality assurance, debugging, and regulatory compliance. In this study, XAI methodologies used in software engineering and decision-making from 2010 to 2025 are systematically evaluated using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework. 705 records were found after searching Scopus, Web of Science, IEEE Xplore, ACM Digital Library, PubMed, and Google Scholar. 98 of these records were included after a rigorous screening procedure. The findings indicate that the most popular strategies in the sector include rule-based techniques, attention-based architectures, SHAP, and LIME. Decision support, risk assessment, optimization, performance evaluation, automated testing, and bug prediction are a few examples of applications. Despite tremendous progress, standardizing assessment metrics, guaranteeing scalability, and incorporating XAI into DevOps pipelines are still difficult problems. This paper creates a conceptual taxonomy of XAI in software engineering and decision-making, brings diverse knowledge together, and lays out a research agenda to create AI solutions that are human-centered, interpretable, and scalable. This paper fills a major research vacuum by combining Explainable AI (XAI) applications from software engineering and decision-making, two fields that have been studied separately. Unlike previous fragmented evaluations, it provides a synthesis (2010–2025) based on PRISMA 2020, outlining a taxonomy of XAI methods, applications, and barriers to human-centered and scaled AI solutions. Overall, being the first PRISMA-based synthesis to systematically connect Explainable AI (XAI) methods with software engineering and decision-making domains, this review fills a critical research vacuum.