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In this study, the authors aimed at prediction with the finer granularity of the portion that induces a bug. Specifically, they tried to predict bug-inducing changes by using source code differences of bug inducing changes and previous changes and a text filtering technique. Similarly, the authors tried to predict bug fixing by using source code differences of bug fixing changes and previous changes and a text filtering technique. To show the effectiveness of the approach, the authors conducted two experiments and compared their approach with fault-prone filtering by applying it to two open source projects, and obtained higher accuracy.<\/p>","DOI":"10.4018\/ijsi.2015010104","type":"journal-article","created":{"date-parts":[[2015,1,8]],"date-time":"2015-01-08T14:53:09Z","timestamp":1420728789000},"page":"50-62","source":"Crossref","is-referenced-by-count":2,"title":["Classification of Bug Injected and Fixed Changes Using a Text Discriminator"],"prefix":"10.4018","volume":"3","author":[{"given":"Akihisa","family":"Yamada","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Kyoto Institute of Technology, Kyoto, Japan"}]},{"given":"Osamu","family":"Mizuno","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Kyoto Institute of Technology, Kyoto, Japan"}]}],"member":"2432","reference":[{"key":"ijsi.2015010104-0","unstructured":"Apache mina. 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