Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault

Chandrasekaran, Jaganmohan; Ghandehari, Laleh Sh.; Lei, Yu; Kacker, Raghu N.; Kuhn, D. Richard

doi:10.1109/icstw.2016.44

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…In addition to framework and method, some PSSs present a tool as a major contribution. For example in [59], a tool named BEN is developed and evaluated on localising different types of software fault. Evaluation or comparison refers to the PSSs evaluating other methods or systems, i.e.…”

Section: Rq2 What Research Approaches (Research Facets) Have Been Fomentioning

confidence: 99%

Software fault localisation: a systematic mapping study

et al. 2019

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Software fault localisation (SFL) is recognised to be one of the most tedious, costly, and critical activities in program debugging. Due to the increase in software complexity, there is a huge interest in advanced SFL techniques that aid software engineers in locating program bugs. This interest paves a way to the existence of a large amount of literature in the SFL research domain. This study aims to investigate the overall research productivity, demographics, and trends shaping the landscape of SFL research domain. The research also aims to classify existing fault localisation techniques and identify trends in the field of study. Accordingly, a systematic mapping study of 273 primary selected studies is conducted with the adoption of an evidence-based systematic methodology to ensure coverage of all relevant studies. The results of this systematic mapping study show that SFL research domain is gaining more attention since 2010, with an increasing number of publications per year. Three main research facets were identified, i.e. validation research, evaluation research, and solution research, with solution research type getting more attention. Hence, various contribution facets were identified as well. In totality, general demographics of SFL research domain were highlighted and discussed.

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Section: Rq2 What Research Approaches (Research Facets) Have Been Fomentioning

confidence: 99%

Software fault localisation: a systematic mapping study

et al. 2019

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“…For example, there exists three partitions with blocks [1,2], [1,2,3], [1,2] for three parameters. Supposing base choice block are '1', '3', '1', the base choice test is [1,3,1], and the following additional tests would be generated:…”

Section: Definition 9 Basic Choice Coverage (Bcc)mentioning

confidence: 99%

“…In recent years, how to use test suite and their testing results to diagnose the root cause of failures has attracted much attention [1,2,8,10]. Nie et al [10] proposed the minimum failure-causing schema, which also is called the minimal failureinducing mode, and highlighted the MFS to help locate and understand the bug(s) in source code [8].…”

Section: Introductionmentioning

confidence: 99%

Pinpoint Minimal Failure-Inducing Mode using Itemset Mining under Constraints

Wang¹

2018

IJPE

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A minimal failure-inducing mode (MFM) based on a t-way combinatorial test set and its test results can help programmers identify root causes of failures that are triggered by combination bugs. However, an MFM for systems containing many parameters may be affected by masking effects to result in coincidences correct in practice, which makes pinpointing MFS more difficult. An approach for pinpointing MFM and an iterative framework are proposed. The identifying MFM approach first collects combinatorial test cases and their testing results, then mines the frequent itemset (suspicious MFM) in failed test cases, and finally computes suspiciousness for each MFM belonged to close pattern via contrasting frequency in failed test cases and successful test cases. Through the iterative framework, MFM is pinpointed until a certain stopping criterion is satisfied. Preliminary results of simulation experiments show that this approach is effective.

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On a Combinatorial Framework for Fault Characterization

Colbourn

Syrotiuk

2018

Math.Comput.Sci.

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Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault

Cited by 9 publications

References 20 publications

Software fault localisation: a systematic mapping study

Software fault localisation: a systematic mapping study

Pinpoint Minimal Failure-Inducing Mode using Itemset Mining under Constraints

On a Combinatorial Framework for Fault Characterization

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