Graph-based supplementary patch recommendation and software reliability prediction by mining software repositories = 소프트웨어 저장소 마이닝을 통한 그래프 기반 부가적인 수정 추천 및 소프트웨어 신뢰성 예측

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As developers have used version control systems and bug tracking systems, the software development process has become more visible and traceable. Since the 2000s, the relevant information has been studied by researchers in the mining software repository area. The studies have suggested various prediction models to improve the quality of software and to reduce the development cost. In this dissertation, two principal problems in the mining software repository are studied, using graph-based approaches. First, we examine the change recommendation approach. Change recommendation approaches have been suggested to prevent omission errors by predicting additional change locations for a given change set. We study a group of bug reports that are fixed more than once to investigate real-world omission errors. We empirically study the characteristics of the multi-fix bugs and how the supplementary patch locations can be predicted based on the initial change locations. Additionally, we suggest a novel graph representation - the change relationship graph - and comprehensively investigate the relationships between initial and supplementary change locations on the change relationship graph. Second, we examine the reliability prediction approach. We suggest a reliability measure to take into account the severity of each released fault, specifically the weighted number of faults. Regression models are used to build prediction models based on existing object-oriented, change, and graph metrics. We investigate the effects of metric sets, feature selection methods, regression models, and the size of the training set on the prediction accuracy. Furthermore, we use structure, clone, and co-change graphs to investigate how these graphs evolve along the release history, as well as how they can be used to predict release-level reliability and fault-prone classes.
Advisors
Bae, Doo-Hwanresearcher배두환researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2016
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전산학부, 2016.8 ,[viii, 97 p :]

Keywords

Software repository mining; Software faults; Change recommendation; Graph-based prediction; Prediction models; 소프트웨어 리포지토리 마이닝; 소프트웨어 결함; 변경 위치 추천; 그래프 기반 예측; 예측 모델

URI
http://hdl.handle.net/10203/222401
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663206&flag=dissertation
Appears in Collection
CS-Theses_Ph.D.(박사논문)
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