한국정보과학회논문지:소프트웨어및응용 (Journal of KIISE:Software and Applications)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

토픽 모델과 소셜 네트워크를 이용한 개발자 추천방법

A Developer Recommendation Technique Based on Topic Model and Social Network

  • 투고 : 2014.03.25
  • 심사 : 2014.05.27
  • 발행 : 2014.08.15
⟨ 이전 논문 다음 논문 ⟩

초록

최근 소프트웨어 규모가 더욱 커지고 복잡해지고 있다. 하루에도 수많은 버그 리포트들이 버그저장소에 전송 되어 개발자들의 업무가 늘어나고 있다. 이러한 버그 리포트들을 적절한 개발자에게 전달하여 빠르고 정확하게 소프트웨어의 결함이 수정되어야 하는데, 많은 버그 리포트들이 적절하지 않는 개발자에게 배정되어 다른 개발자에게 다시 재배정 되는 경우가 빈번하게 일어나고 있다. 이것은 배정자가 전송받은 버그 리포트들을 정확히 이해하지 못했거나, 또는 모든 개발자들의 능력을 바르게 파악하지 못해 발생한다. 이것은 소프트웨어 유지보수에 개발자의 시간과 노력을 많이 필요하게 한다. 이러한 문제를 해결하기 위해 본 연구에서는 버그 리포트와 관련된 토픽을 찾아내고, 토픽 내 개발자들의 소셜 네트워크 관계를 분석해서 적절한 개발자를 추천하는 기법을 제안한다. 그리고 공개 소스 프로젝트를 이용한 개발자 추천에 대한 성능비교 실험을 통하여 본 연구에서 제안한 방법이 효과적이라는 것을 보인다.

Recently, software projects have been increasing and getting complex. Due to the large number of submitted bug reports, developers' workload increases. Generally in bug triage process, the triagers assign the bug report to fixer (developer) in order to resolve the bug. However, bug reports have been reassigned to other developers because fixers are not suitable. This is why the triagers did not correctly check and understand the bug report and decide the appropriate developers to fix the bug. This results in increase of developers' time and efforts in software maintenance. To resolve these problems, in this paper, we propose a novel method for developer recommendation based on topic model and social network. First, we build a basis of topic(s) from bug reports. Next, when a new bug report (test data set) comes, we select the most similar topic(s) and extract the participated developers from the topic(s). Finally, by applying social network, we analyze the developers' behavior (comment and commit activity) and recommend the appropriate developers. In this paper we compare our work with related studies through performance experiments on open source projects. The results show that our approach is more effective than other studies in bug triage.

키워드

과제정보

연구 과제 주관 기관 : 서울시립대학교

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