{"title":"“你的应用程序的哪些部分受到用户的喜爱?”(T)","authors":"Xiaodong Gu, Sunghun Kim","doi":"10.1109/ASE.2015.57","DOIUrl":null,"url":null,"abstract":"Recently, Begel et al. found that one of the most important questions software developers ask is \"what parts of software are used/loved by users.\" User reviews provide an effective channel to address this question. However, most existing review summarization tools treat reviews as bags-of-words (i.e., mixed review categories) and are limited to extract software aspects and user preferences. We present a novel review summarization framework, SUR-Miner. Instead of a bags-of-words assumption, it classifies reviews into five categories and extracts aspects for sentences which include aspect evaluation using a pattern-based parser. Then, SUR-Miner visualizes the summaries using two interactive diagrams. Our evaluation on seventeen popular apps shows that SUR-Miner summarizes more accurate and clearer aspects than state-of-the-art techniques, with an F1-score of 0.81, significantly greater than that of ReviewSpotlight (0.56) and Guzmans' method (0.55). Feedback from developers shows that 88% developers agreed with the usefulness of the summaries from SUR-Miner.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"51 1","pages":"760-770"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"151","resultStr":"{\"title\":\"\\\"What Parts of Your Apps are Loved by Users?\\\" (T)\",\"authors\":\"Xiaodong Gu, Sunghun Kim\",\"doi\":\"10.1109/ASE.2015.57\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, Begel et al. found that one of the most important questions software developers ask is \\\"what parts of software are used/loved by users.\\\" User reviews provide an effective channel to address this question. However, most existing review summarization tools treat reviews as bags-of-words (i.e., mixed review categories) and are limited to extract software aspects and user preferences. We present a novel review summarization framework, SUR-Miner. Instead of a bags-of-words assumption, it classifies reviews into five categories and extracts aspects for sentences which include aspect evaluation using a pattern-based parser. Then, SUR-Miner visualizes the summaries using two interactive diagrams. Our evaluation on seventeen popular apps shows that SUR-Miner summarizes more accurate and clearer aspects than state-of-the-art techniques, with an F1-score of 0.81, significantly greater than that of ReviewSpotlight (0.56) and Guzmans' method (0.55). Feedback from developers shows that 88% developers agreed with the usefulness of the summaries from SUR-Miner.\",\"PeriodicalId\":6586,\"journal\":{\"name\":\"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"volume\":\"51 1\",\"pages\":\"760-770\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"151\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASE.2015.57\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASE.2015.57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recently, Begel et al. found that one of the most important questions software developers ask is "what parts of software are used/loved by users." User reviews provide an effective channel to address this question. However, most existing review summarization tools treat reviews as bags-of-words (i.e., mixed review categories) and are limited to extract software aspects and user preferences. We present a novel review summarization framework, SUR-Miner. Instead of a bags-of-words assumption, it classifies reviews into five categories and extracts aspects for sentences which include aspect evaluation using a pattern-based parser. Then, SUR-Miner visualizes the summaries using two interactive diagrams. Our evaluation on seventeen popular apps shows that SUR-Miner summarizes more accurate and clearer aspects than state-of-the-art techniques, with an F1-score of 0.81, significantly greater than that of ReviewSpotlight (0.56) and Guzmans' method (0.55). Feedback from developers shows that 88% developers agreed with the usefulness of the summaries from SUR-Miner.