Effective Statistical Fault Localization Using Program Slices

Lei, Yan; Mao, Xiaoguang; Dai, Ziying; Wang, Chengsong

doi:10.1109/compsac.2012.9

Cited by 38 publications

(27 citation statements)

References 29 publications

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“…Among other results, their approach was used to uncover two real bugs in the Siemens Suite [138]. In a related project, Lei et al [139] applied the same idea to address the oracle problem in a variant of SBFL named Backward-Slice Statistical Fault Localisation (BSSFL) [140]. Rao et al [141] investigated the ratio between non-violated and violated metamorphic relations in SBFL.…”

Section: Other Testing Applicationsmentioning

confidence: 99%

A Survey on Metamorphic Testing

Segura

Fraser

Sánchez

et al. 2016

IIEEE Trans. Software Eng.

404

230

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Abstract-A test oracle determines whether a test execution reveals a fault, often by comparing the observed program output to the expected output. This is not always practical, for example when a program's input-output relation is complex and difficult to capture formally. Metamorphic testing provides an alternative, where correctness is not determined by checking an individual concrete output, but by applying a transformation to a test input and observing how the program output "morphs" into a different one as a result. Since the introduction of such metamorphic relations in 1998, many contributions on metamorphic testing have been made, and the technique has seen successful applications in a variety of domains, ranging from web services to computer graphics. This article provides a comprehensive survey on metamorphic testing: It summarises the research results and application areas, and analyses common practice in empirical studies of metamorphic testing as well as the main open challenges.

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Section: Other Testing Applicationsmentioning

confidence: 99%

A Survey on Metamorphic Testing

Segura

Fraser

Sánchez

et al. 2016

IIEEE Trans. Software Eng.

404

230

View full text Add to dashboard Cite

show abstract

“…Future work could also consider aggregation of data from multiple failing runs in, for example, slice-based fault localization (e.g. [26]) or some form of union slicing (e.g. [28]).…”

Section: Discussionmentioning

confidence: 99%

Lightweight control-flow instrumentation and postmortem analysis in support of debugging

Ohmann

Liblit

2016

Autom Softw Eng

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Abstract-Debugging is difficult and costly. As a human programmer looks for a bug, it would be helpful to see a complete trace of events leading to the point of failure. Unfortunately, full tracing is simply too slow to use in deployment, and may even be impractical during testing.We aid post-deployment debugging by giving programmers additional information about program activity shortly before failure. We use latent information in post-failure memory dumps, augmented by low-overhead, tunable run-time tracing. Our results with a realistically-tuned tracing scheme show low enough overhead (0-5%) to be used in production runs. We demonstrate several potential uses of this enhanced information, including a novel postmortem static slice restriction technique and a reduced view of potentially-executed code. Experimental evaluation shows our approach to be very effective, such as shrinking stack-sensitive interprocedural static slices by 49-78% in larger applications.

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“…However, SFL just outputs a ranked list of isolated entities and fails to provide the contextual information for discovering and understanding how suspicious statements act on each other to trigger failure. Moreover, SFL uses code coverage that cannot distinguish whether the execution of a statement affected the output [4]. However, the execution of faulty statements should generally trigger and affect the faulty output of a program when a failure occurs.…”

Section: Introductionmentioning

confidence: 99%

“…Program slicing techniques utilize data and control dependencies to identify a set of statements that may affect the value at a specific statement of a program [4], [6]. The set of statements is denoted as a program slice.…”

Section: Introductionmentioning

confidence: 99%