2020/12/03 by Chakkrit Tantithamthavorn, Tantithamthavorn, Chakkrit, Jirayus Jiarpakdee +3 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Software Engineering Techniques and Practices #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2012.01614
openalex publication_date 2020/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Artificial Intelligence/Machine Learning techniques have been widely used in software engineering to improve developer productivity, the quality of software systems, and decision-making. However, such AI/ML models for software engineering are still impractical, not explainable, and not actionable. These concerns often hinder the adoption of AI/ML models in software engineering practices. In this article, we first highlight the need for explainable AI in software engineering. Then, we summarize three successful case studies on how explainable AI techniques can be used to address the aforementioned challenges by making software defect prediction models more practical, explainable, and actionable.