DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yoo, Shin | - |
dc.contributor.advisor | 유신 | - |
dc.contributor.author | An, Gabin | - |
dc.date.accessioned | 2021-05-13T19:32:22Z | - |
dc.date.available | 2021-05-13T19:32:22Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910995&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/284668 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전산학부, 2020.2,[iv, 32 p. :] | - |
dc.description.abstract | Many Fault Localisation (FL) techniques have been developed to alleviate the developers debugging cost. Spectrum-Based FL (SBFL) is known as the most effective family as a standalone FL technique. However, summarising program coverage information into program spectrum often results in huge loss of information such as 'which element is covered by which test?' or 'which elements are always executed together?'. In this work, we propose to preserve the original information as much as possible and let a classifier model learn the coverage pattern of failing tests. Once the training is done, we extract the suspiciousness scores from the trained classifier. Pursuing more effective learning from the coverage data, we use the random over-sampling method to mitigate the class imbalance problem and also try to reflect the execution frequency information in training set by using a new regularisation method. As a result, we found that our method can significantly increase the FL performance, especially when using an Artificial Neural Network classifier. On the Defect4J benchmark, some of our best-performing models can localise about 20 more faults at the top place than the state-of-the-art SBFL formulae such as Ochiai and Op2. We also found that hybridising our technique with well-performing SBFL formulae would further improve the overall FL performance than using each of them individually. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 커버리지기반결함위치식별▼a분류▼a다중신경망▼a로지스틱회귀▼a클래스불균형 | - |
dc.subject | Fault Localisation▼aCoverage-based Fault Localisation▼aClassification▼aDeep Neural Network▼aLogistic Regression▼aClass Imbalance | - |
dc.title | Localising software faults by learning patterns of failing executions | - |
dc.title.alternative | 실행 오류 패턴 학습을 통한 소프트웨어 결함 위치 식별 기술 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 안가빈 | - |
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