Yoon Ho Choi  Changgong Lee  Jaechang Nam

10.5626/JOK.2022.49.8.633

Artificial intelligence software defect Empirical Study mining software repositories

The differences between the programming paradigm of applications using artificial intelligence (AI) and traditional applications may show different results in detecting, understanding, analyzing, and fixing defects. In this study, we collect defects that have been reported in open source AI applications and identify common causes of the defects to understand and analyze them in AI-based systems. To this end, we analyze the defects of ten open-source AI applications archived on GitHub by inspecting 1,205 issues and defect-fixing code changes that had been reported, found, and fixed. We classified the defects into 20 categories based on their causes, which are found in at least five out of ten projects. We expect that the result of this study will provide useful information in software quality assurance approaches such as fault localization and patch suggestion.