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SPI: Similar Patch Identifier for Automated Program Repair
Sechang Jang, Seongbin Kim, Junhyeok Choi, Jindae Kim, Jaechang Nam
http://doi.org/10.5626/JOK.2025.52.2.152
The primary challenge in Automated Program Repair (APR) techniques is the size of search space. In this study, we introduce a novel approach called Similar Patch Identifier (SPI), which reduces the search space by leveraging the similarities among bug-introducing changes and suggesting suitable repair operators. We evaluate this approach using the existing context-based APR tool, ConFix, and the Java defect benchmark, Defects4J. Our experiments revealed that, although SPI narrowed the search space to 10 candidate bug-fixing commits for each defect, it successfully generated meaningful patches for four bugs that ConFix was unable to repair.
An Empirical Study of MISRA-C Related Source Code Changes in Open-source Software Projects
Suhyun Park, Jaechang Nam, Shin Hong
http://doi.org/10.5626/JOK.2024.51.8.718
This paper presents empirical studies on 75 open-source projects hosted on GitHub to explore how source code changes align with MISRA C coding guidelines. Through our analysis of the studied projects, we have identified eight distinctive keywords that represent the software domains where compliance with MISRA C coding guidelines is likely to be found. Additionally, we discovered that 54.75% of the studied projects utilizes at least one static rule checker. In the 75 studied projects, we found code changes associated with 75 MISRA C coding rules. The analyses of these code changes reveal that multiple MISRA C-related code changes often occur in a short timeframe, and, on average, each MISRA C-related code change modifies 1124 lines of code at once.
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.
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