The DStar Method for Effective Software Fault Localization

By W. E. Wong, Vidroha Debroy, Ruizhi Gao, Yihao Li. IEEE Transactions on Reliability, 2014

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Evaluating and Improving Fault Localization (2017, methodology): “The other 60% of the results are statistically insigniﬁcant; for example, DStar is better than Tarantula on artiﬁcial faults [19], [26], [44], but on real faults there is no signiﬁcant difference between the two techniques.”
A Survey on Software Fault Localization (2016, methodology): “Tool for collecting execution trace information for C programs via author [22], [366], [367], [369], [370], [371], [372], [373], [374] x Vue Heuristics involving the control graph, execution trace, and the maintainer’s knowledge to help locate features and identify feature interactions via author…”
TBar: revisiting template-based automated program repair (2019, background): “, Trantula [15], Ochiai [2], Op2 [47], Barinel [3], Dstar[66]) to calculate the suspiciousness of each statement.”
HyperFL: Query-Adaptive Representation Learning for Software Fault Localization (2026, unknown): “…to their statistical association with passing and failing test executions (Abreu, Zoeteweij, and Van Gemund 2007; Bandyopadhyay and Ghosh 2011; Wong et al. 2013 Information-retrieval-based FL instead treats a bug report as a natural-language query and ranks source-code entities according to…”