Visualization of test information to assist fault localization

By James A. Jones, Mary Jean Harrold, John T. Stasko. Proceedings of the 24th International Conference on Software Engineering. ICSE 2002, 2002

1,271 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/54a7d105ee1a5256b168f16d8264262340c0f6c3

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Citations in the corpus

Metallaxis‐FL: mutation‐based fault localization (2015, methodology): “Researchers have used many coverage elements such as program statements [9], [10], [11] program branches [12], [8] program du-pairs [13] and possible combinations of them [8], [14].”
SQLizer: query synthesis from natural language (2017, methodology): “…refinement strategy in Sqlizer is inspired by prior work on fault localization [Ball et al. 2003; Groce and Visser 2003; Jones and Harrold 2005; Jones et al. 2002; Jose and Majumdar 2011a,b] and program repair [Goues et al. 2012; Long and Rinard 2015, 2016; Nguyen et al. 2013; Weimer et al.…”
BugFix: A learning-based tool to assist developers in fixing bugs (2009, methodology): “Other approaches [17, 21, 24, 25] use runtime information to rank program entities in the order of their likelihood of being faulty.”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, methodology): “Unlike spectrum-based fault localization techniques [1, 18, 19, 22, 23], information retrieval (IR) based bug localization does not require program execution information (such as passing and failing traces).”
Empirical evaluation of the tarantula automatic fault-localization technique (2005, methodology): “However, their studies do not include a comparison of the Tarantula technique—another existing technique [5] that also computes a ranking of statements in terms of their suspiciousness.”
SOBER: statistical model-based bug localization (2005, background): “Similar information is later visualized in [12], making it more accessible to the programmers.”
Statistical Debugging: A Hypothesis Testing-Based Approach (2006, methodology): “In a recent study [8], the authors took ð1 hueðsÞÞ as the fault relevance score for the statement s, where hueðsÞ is the hue component of each statement in visualization [23].”
A model for spectra-based software diagnosis (2011, background): “Tarantula [Jones et al. 2002; Jones and Harrold 2005] is normally credited as the .rst spectra-based debugger.”
An Evaluation of Similarity Coefficients for Software Fault Localization (2006, methodology): “The Tarantula system [12, 13] has been developed for the C language, and applies the technique of the previous section to statement hit spectra.”
A Survey on Software Fault Localization (2016, methodology): “Based on the suspiciousness computed by Tarantula, studies like [186], [187] use different colors (from red to yellow to green) to provide a visual mapping of the participation of each program statement in the execution of a test suite.”
A practical evaluation of spectrum-based fault localization (2009, methodology): “…of tools that implement this approach are Pinpoint (Chen et al., 2002), which focuses on large, dynamic on-line transaction processing systems (Jones et al., 2002) whose implementation focuses on the analysis of C programs, and AMPLE (Dallmeier et al., 2005), which focuses on object-oriented…”
Automated program repair (2019, background): “, [12], provides a ranking of code locations that indicates their potential buggy-ness.”
Fault localization with nearest neighbor queries (2003, background): “[17] provide such a ranking, but their technique assumes multiple faulty runs, which is problematic in the case of multiple faults.”
Precise Condition Synthesis for Program Repair (2016, methodology): “first apply spectrum-based fault localization [24] to locate a list of potentially faulty methods.”
SemFix: Program repair via semantic analysis (2013, methodology): “Statistical fault localization [9], [13] aims to localize the root-cause of a program failure by exploiting the correlation between execution of the faulty statements and program failure.”
Isolating cause-effect chains from computer programs (2002, background): “Our own contributions [1] as well as inferring relationships between code and tests [15] have already been mentioned.”