Enhancing Fault Localization with Cost Awareness

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dc.contributor.authorNachimuthu Nallasamy, Kanagarajen
dc.contributor.committeechairServant Cortes, Francisco Javieren
dc.contributor.committeememberPrakash, B. Adityaen
dc.contributor.committeememberMeng, Naen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2019-06-25T08:00:51Zen
dc.date.available2019-06-25T08:00:51Zen
dc.date.issued2019-06-24en
dc.description.abstractDebugging is a challenging and time-consuming process in software life-cycle. The focus of the thesis is to improve the accuracy of existing fault localization (FL) techniques. We experimented with several source code line level features such as line commit size, line recency, and line length to arrive at a new fault localization technique. Based on our experiments, we propose a novel enhanced cost-aware fault localization (ECFL) technique by combining line length with the existing selected baseline fault localization techniques. ECFL improves the accuracy of DStar (Baseline 1), CombineFastestFL (Baseline 2), and CombineFL (Baseline 3) by locating 81%, 58%, and 30% more real faults respectively in Top-1 evaluation metric. In comparison with the baseline techniques, ECFL requires a marginal additional time (on an average, 5 seconds per bug) and data while providing a significant improvement in accuracy. The source code line features also improve the baseline fault localization techniques when ''learning to rank'' SVM machine learning approach is used to combine the features. We also provide an infrastructure to facilitate future research on combining new source code line features with other fault localization techniques.en
dc.description.abstractgeneralSoftware debugging involves locating and fixing faults (or bugs) in software. It is a challenging and time-consuming process in software life-cycle. Fault localization (FL) techniques help software developers to locate faults by providing a ranked set of program elements. The focus of the thesis is to improve the accuracy of existing fault localization techniques. We experimented with several source code line level features such as line commit size, line recency, and line length to arrive at a new fault localization technique. Based on our experiments, we propose a novel enhanced cost-aware fault localization (ECFL) technique by combining line length with the existing selected baseline fault localization techniques. ECFL improves the accuracy of DStar (Baseline 1), CombineFastestFL (Baseline 2), and CombineFL (Baseline 3) by locating 81%, 58%, and 30% more real faults respectively in Top-1 evaluation metric. In comparison with the baseline techniques, ECFL requires a marginal additional time (on an average, 5 seconds per bug) and data while providing a significant improvement in accuracy. The source code line features also improve the baseline fault localization techniques when machine learning approach is used to combine the features. We also provide an infrastructure to facilitate future research on combining new source code line features with other fault localization techniques.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:21387en
dc.identifier.urihttp://hdl.handle.net/10919/90575en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectfault localizationen
dc.subjectautomated debuggingen
dc.subjectsource code line featuresen
dc.subjectcost-aware fault localizationen
dc.titleEnhancing Fault Localization with Cost Awarenessen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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