Digital Library[ Search Result ]
Change Description Difference Analysis between Human and Code Differencing Techniques
Moojun Kim, Beomcheol Kim, Jindae Kim
http://doi.org/10.5626/JOK.2023.50.2.150
This study investigated the difference between descriptions of code changes made by source code differencing tools and humans. We applied two popular source code differencing techniques to collected code changes. We found that these tools often generated different descriptions for the same changes, and only 3% of the changes have the same descriptions from both tools. On the other hand, human participants agree on change descriptions for 50% of the given changes. Furthermore, many of the different descriptions were caused by simple mistakes. If we ignore differences caused by these mistakes, human participants described 71% of the changes similarly. We also compared change type and entity type of edit scripts generated by human and the source code differencing techniques for the same changes. We found that the techniques generated the same description as humans for only 8.20～35.65% of the changes, which indicates that these techniques require significant improvement to provide descriptions similar to human’s.
An Empirical Study on Defects in Open Source Artificial Intelligence Applications
Yoon Ho Choi, Changgong Lee, Jaechang Nam
http://doi.org/10.5626/JOK.2022.49.8.633
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.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr