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dc.contributor.authorNachimuthu Nallasamy, Kanagarajen_US
dc.date.accessioned2019-06-25T08:00:51Z
dc.date.available2019-06-25T08:00:51Z
dc.date.issued2019-06-24
dc.identifier.othervt_gsexam:21387en_US
dc.identifier.urihttp://hdl.handle.net/10919/90575
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_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectfault localizationen_US
dc.subjectautomated debuggingen_US
dc.subjectsource code line featuresen_US
dc.subjectcost-aware fault localizationen_US
dc.titleEnhancing Fault Localization with Cost Awarenessen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairServant Cortes, Francisco Javieren_US
dc.contributor.committeememberPrakash, Bodicherla Adityaen_US
dc.contributor.committeememberMeng, Naen_US
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


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