{"title":"挖掘软件存储库以促进重构的研究","authors":"Ally S. Nyamawe","doi":"10.1002/widm.1508","DOIUrl":null,"url":null,"abstract":"Software refactoring focuses on improving software quality by applying changes to the internal structure that do not alter the observable behavior. Determining which refactorings should be applied and presented to developers the most relevant and optimal refactorings is often challenging. Existing literature suggests that one of the potential sources to identify and recommend required refactorings is the past software development and evolution histories which are often archived in software repositories. In this article, we review a selection of existing literature that has attempted to propose approaches that facilitate refactoring by exploiting information mined from software repositories. Based on the reviewed papers, existing works leverage software history mining to support analysis of code smells, refactoring, and guiding software changes. First, past history information is used to detect design flaws in source code commonly referred to as code smells. Moreover, other studies analyze the evolution of code smells to establish how and when they are introduced into the code base and get resolved. Second, software repositories mining provides useful insights that can be used in predicting the need for refactoring and what specific refactoring operations are required. In addition, past history can be used in detecting and analyzing previously applied refactorings to establish software change facts, for instance, how developers refactor code and the motivation behind it. Finally, change patterns are used to predict further changes that might be required and recommend a set of files for change during a given modification task. The paper further suggests other exciting possibilities that can be pursued in the future in this research direction.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"130 1","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on mining software repositories to facilitate refactoring\",\"authors\":\"Ally S. Nyamawe\",\"doi\":\"10.1002/widm.1508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software refactoring focuses on improving software quality by applying changes to the internal structure that do not alter the observable behavior. Determining which refactorings should be applied and presented to developers the most relevant and optimal refactorings is often challenging. Existing literature suggests that one of the potential sources to identify and recommend required refactorings is the past software development and evolution histories which are often archived in software repositories. In this article, we review a selection of existing literature that has attempted to propose approaches that facilitate refactoring by exploiting information mined from software repositories. Based on the reviewed papers, existing works leverage software history mining to support analysis of code smells, refactoring, and guiding software changes. First, past history information is used to detect design flaws in source code commonly referred to as code smells. Moreover, other studies analyze the evolution of code smells to establish how and when they are introduced into the code base and get resolved. Second, software repositories mining provides useful insights that can be used in predicting the need for refactoring and what specific refactoring operations are required. In addition, past history can be used in detecting and analyzing previously applied refactorings to establish software change facts, for instance, how developers refactor code and the motivation behind it. Finally, change patterns are used to predict further changes that might be required and recommend a set of files for change during a given modification task. The paper further suggests other exciting possibilities that can be pursued in the future in this research direction.\",\"PeriodicalId\":48970,\"journal\":{\"name\":\"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery\",\"volume\":\"130 1\",\"pages\":\"\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2023-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1002/widm.1508\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/widm.1508","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Research on mining software repositories to facilitate refactoring
Software refactoring focuses on improving software quality by applying changes to the internal structure that do not alter the observable behavior. Determining which refactorings should be applied and presented to developers the most relevant and optimal refactorings is often challenging. Existing literature suggests that one of the potential sources to identify and recommend required refactorings is the past software development and evolution histories which are often archived in software repositories. In this article, we review a selection of existing literature that has attempted to propose approaches that facilitate refactoring by exploiting information mined from software repositories. Based on the reviewed papers, existing works leverage software history mining to support analysis of code smells, refactoring, and guiding software changes. First, past history information is used to detect design flaws in source code commonly referred to as code smells. Moreover, other studies analyze the evolution of code smells to establish how and when they are introduced into the code base and get resolved. Second, software repositories mining provides useful insights that can be used in predicting the need for refactoring and what specific refactoring operations are required. In addition, past history can be used in detecting and analyzing previously applied refactorings to establish software change facts, for instance, how developers refactor code and the motivation behind it. Finally, change patterns are used to predict further changes that might be required and recommend a set of files for change during a given modification task. The paper further suggests other exciting possibilities that can be pursued in the future in this research direction.
期刊介绍:
The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.