Explainable AI for Software Engineering
- URL: http://arxiv.org/abs/2012.01614v1
- Date: Thu, 3 Dec 2020 00:42:29 GMT
- Title: Explainable AI for Software Engineering
- Authors: Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, John Grundy
- Abstract summary: 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.
- Score: 12.552048647904591
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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