P. McBurney, Cheng Liu, Collin McMillan, Tim Weninger
{"title":"Improving topic model source code summarization","authors":"P. McBurney, Cheng Liu, Collin McMillan, Tim Weninger","doi":"10.1145/2597008.2597793","DOIUrl":null,"url":null,"abstract":"In this paper, we present an emerging source code summarization technique that uses topic modeling to select keywords and topics as summaries for source code. Our approach organizes the topics in source code into a hierarchy, with more general topics near the top of the hierarchy. In this way, we present the software's highest-level functionality first, before lower-level details. This is an advantage over previous approaches based on topic models, that only present groups of related keywords without a hierarchy. We conducted a preliminary user study that found our approach selects keywords and topics that the participants found to be accurate in a majority of cases.","PeriodicalId":6853,"journal":{"name":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","volume":"26 29","pages":"291-294"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"43","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2597008.2597793","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 43
Abstract
In this paper, we present an emerging source code summarization technique that uses topic modeling to select keywords and topics as summaries for source code. Our approach organizes the topics in source code into a hierarchy, with more general topics near the top of the hierarchy. In this way, we present the software's highest-level functionality first, before lower-level details. This is an advantage over previous approaches based on topic models, that only present groups of related keywords without a hierarchy. We conducted a preliminary user study that found our approach selects keywords and topics that the participants found to be accurate in a majority of cases.