Statistical Debugging: A Hypothesis Testing-Based Approach

By Chao Liu, Long Fei, Xifeng Yan, Jiawei Han, Samuel P. Midkiff. IEEE Transactions on Software Engineering, 2006

318 citations recorded by the source corpus.

How researchers see this paper

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Software Reliability Engineering: A Roadmap (2007, background): “In particular, statistical debugging approaches [26, 52], whose original purpose was to identify software faults with probabilistic modeling of program predicates, can provide a fine quantitative assessment of program codes with respect to software faults.”
The DStar Method for Effective Software Fault Localization (2014, methodology): “Because it is not possible to theoretically prove if one fault localization technique shall always be more effective than another, such empirical validation is typically the norm for fault localization studies [1], [3], [8], [23], [25], [31], [39], [40], [42].”
A Survey on Software Fault Localization (2016, methodology): “The T-score [223], [303] estimates the percentage of code a programmer need not examine before the ﬁrst faulty location is found.”
TBar: revisiting template-based automated program repair (2019, methodology): “…defect locations in a program, several automated FL techniques have been proposed [67]: slice-based [41, 65], spectrum-based [3, 52], statistics-based [28, 29], etc. Spectrum-based FL (SBFL) techniques are widely adopted in APR pipelines since they identify bug position at the statement level.”
Evaluating and Improving Fault Localization (2017, background): “This section explains how a fault localization technique’s output can be evaluated.”