Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Study on Automatic Bug Triage using Deep Learning

딥 러닝을 이용한 버그 담당자 자동 배정 연구

  • 이선로 (중앙대학교 컴퓨터공학과) ;
  • 김혜민 (중앙대학교 컴퓨터공학과) ;
  • 이찬근 (중앙대학교 컴퓨터공학부) ;
  • 이기성 (중앙대학교 다빈치교양대학)
  • Received : 2017.04.04
  • Published : 2017.11.15
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Abstract

Existing studies on automatic bug triage were mostly used the method of designing the prediction system based on the machine learning algorithm. Therefore, it can be said that applying a high-performance machine learning model is the core of the performance of the automatic bug triage system. In the related research, machine learning models that have high performance are mainly used, such as SVM and Naïve Bayes. In this paper, we apply Deep Learning, which has recently shown good performance in the field of machine learning, to automatic bug triage and evaluate its performance. Experimental results show that the Deep Learning based Bug Triage system achieves 48% accuracy in active developer experiments, un improvement of up to 69% over than conventional machine learning techniques.

기존의 버그 담당자 자동 배정 연구들은 대부분 기계학습 알고리즘을 기반으로 예측 시스템을 구축하는 방식이었다. 따라서, 고성능의 기계학습 모델을 적용하는 것이 담당자 자동 배정 시스템 성능의 핵심이 된다고 할 수 있으며 관련 연구에서는 높은 성능을 보이는 SVM, Naive Bayes 등의 기계학습 모델들이 주로 사용되고 있다. 본 논문에서는 기계학습 분야에서 최근 좋은 성능을 보이고 있는 딥 러닝을 버그 담당자 자동 배정에 적용하고 그 성능을 평가한다. 실험 결과, 딥 러닝 기반 Bug Triage 시스템이 활성 개발자 대상 실험에서 48%의 정확도를 달성했으며 이는 기존의 기계학습 대비 최대 69%향상된 결과이다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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