{"title":"一个在提交时进行软件缺陷预防的项目:一个大学-工业研究合作的成功案例","authors":"A. Hamou-Lhadj, Mathieu Nayrolles","doi":"10.1145/3195546.3206423","DOIUrl":null,"url":null,"abstract":"In this talk, we describe a research collaboration project between Concordia University and Ubisoft. The project consists of investigating techniques for defect prevention at commit-time for increased software quality. The outcome of this project is a tool called CLEVER (Combining Levels of Bug Prevention and Resolution techniques) that uses machine learning to automatically detect coding defects as programmers write code. The main novelty of CLEVER is that it relies on code matching techniques to detect coding mistakes based on a database of historical code defects found in multiple related projects. The tool also proposes fixes based on known patterns.","PeriodicalId":178576,"journal":{"name":"2018 IEEE/ACM 5th International Workshop on Software Engineering Research and Industrial Practice (SER&IP)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Project on Software Defect Prevention at Commit-Time: A Success Story of University-Industry Research Collaboration\",\"authors\":\"A. Hamou-Lhadj, Mathieu Nayrolles\",\"doi\":\"10.1145/3195546.3206423\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this talk, we describe a research collaboration project between Concordia University and Ubisoft. The project consists of investigating techniques for defect prevention at commit-time for increased software quality. The outcome of this project is a tool called CLEVER (Combining Levels of Bug Prevention and Resolution techniques) that uses machine learning to automatically detect coding defects as programmers write code. The main novelty of CLEVER is that it relies on code matching techniques to detect coding mistakes based on a database of historical code defects found in multiple related projects. The tool also proposes fixes based on known patterns.\",\"PeriodicalId\":178576,\"journal\":{\"name\":\"2018 IEEE/ACM 5th International Workshop on Software Engineering Research and Industrial Practice (SER&IP)\",\"volume\":\"187 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE/ACM 5th International Workshop on Software Engineering Research and Industrial Practice (SER&IP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3195546.3206423\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/ACM 5th International Workshop on Software Engineering Research and Industrial Practice (SER&IP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3195546.3206423","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Project on Software Defect Prevention at Commit-Time: A Success Story of University-Industry Research Collaboration
In this talk, we describe a research collaboration project between Concordia University and Ubisoft. The project consists of investigating techniques for defect prevention at commit-time for increased software quality. The outcome of this project is a tool called CLEVER (Combining Levels of Bug Prevention and Resolution techniques) that uses machine learning to automatically detect coding defects as programmers write code. The main novelty of CLEVER is that it relies on code matching techniques to detect coding mistakes based on a database of historical code defects found in multiple related projects. The tool also proposes fixes based on known patterns.