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- W3134110314 abstract "Fault localization has been determined as a major resource factor in the software development life cycle. Academic fault localization techniques are mostly unknown and unused in professional environments. Although manual debugging approaches can vary significantly depending on bug type (e.g. memory bugs or semantic bugs), these differences are not reflected in most existing fault localization tools. Little research has gone into automated identification of bug types to optimize the fault localization process. Further, existing fault localization techniques leverage on historical data only for augmentation of suspiciousness rankings. This thesis aims to provide a fault localization framework by combining data from various sources to help developers in the fault localization process. To achieve this, a bug classification schema is introduced, benchmarks are created, and a novel fault localization method based on historical data is proposed." @default.
- W3134110314 created "2021-03-15" @default.
- W3134110314 creator A5052733976 @default.
- W3134110314 date "2020-10-01" @default.
- W3134110314 modified "2023-10-17" @default.
- W3134110314 title "A Fault Localization and Debugging Support Framework driven by Bug Tracking Data" @default.
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- W3134110314 doi "https://doi.org/10.1109/issrew51248.2020.00053" @default.
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