A Survey on Software Fault Localization

Wong, W. Eric; Gao, Ruizhi; Li, Yihao; Abreu, Rui; Wotawa, Franz

doi:10.1109/tse.2016.2521368

Cited by 796 publications

(569 citation statements)

References 310 publications

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“…Beyond the original application in program optimization (Ball et al 1998), more recently code profiling information has been used to analyze the executions of different versions of code, e.g., in regression testing (Xie and Notkin 2005), and to compare traces of failed and successful runs in fault diagnosis (Wong et al 2016). Here, we propose to exploit traces information to tune coverage measures onto the usage profile.…”

Section: Related Workmentioning

confidence: 99%

“…The idea of using program spectra in software engineering tasks is not new: program spectra have been used, among others, for regression testing (Xie and Notkin 2005) and fault localization (Wong et al 2016). To the best of our knowledge, however, our research is the first attempt to tune coverage measures based on program spectra, for the purpose of reflecting the importance of program entities.…”

Section: Introductionmentioning

confidence: 99%

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An assessment of operational coverage as both an adequacy and a selection criterion for operational profile based testing

Miranda

Bertolino²

2017

Software Qual J

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While the relation between code coverage measures and fault detection is actively studied, only few works have investigated the correlation between measures of coverage and of reliability. In this work, we introduce a novel approach to measuring code coverage, called the operational coverage, that takes into account how much the program's entities are exercised so to reflect the profile of usage into the measure of coverage. Operational coverage is proposed as (i) an adequacy criterion, i.e., to assess the thoroughness of a black box test suite derived from the operational profile, and as (ii) a selection criterion, i.e., to select test cases for operational profile-based testing. Our empirical evaluation showed that operational coverage is better correlated than traditional coverage with the probability that the next test case derived according to the user's profile will not fail. This result suggests that our approach could provide a good stopping rule for operational profile-based testing. With respect to test case selection, our investigations revealed that operational coverage outperformed the traditional one in terms of test suite size and fault detection capability when we look at the average results.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An assessment of operational coverage as both an adequacy and a selection criterion for operational profile based testing

Miranda

Bertolino²

2017

Software Qual J

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“…RQ2: Highlighting Unsafe Static Slices. RQ2 uses the small programs shown in Table I, known as the Siemens Suite which have been widely-studied in previous work on analysis and manipulation [50]. Each of the seven programs comes with its own pre-defined test suite.…”

Section: Research Questionsmentioning

confidence: 99%

ORBS and the limits of static slicing

Binkley

Gold

Harman

et al. 2015

2015 IEEE 15th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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Abstract-Observation-based slicing is a recently-introduced, language-independent, slicing technique based on the dependencies observable from program behaviour. Due to the wellknown limits of dynamic analysis, we may only compute an under-approximation of the true observation-based slice. However, because the observation-based slice captures all possible dependence that can be observed, even such approximations can yield insight into the limitations of static slicing. For example, a static slice, S that is strictly smaller than the corresponding observation based slice is guaranteed to be unsafe. We present the results of three sets of experiments on 12 different programs, including benchmarks and larger programs, which investigate the relationship between static and observation-based slicing. We show that, in extreme cases, observation-based slices can find the true static minimal slice, where static techniques cannot. For more typical cases, our results illustrate the potential for observation-based slicing to highlight unsafe static slices. Finally, we report on the sensitivity of observation-based slicing to test quality.

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“…However, debugging activity is an expensive and time-consuming process. Thus, many efforts have been made to provide assistance in guiding programmers to the locations of faults that cause incorrect outputs [1]. The core work of fault localization is to evaluate which statement being suspiciously faulty.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fault Localization with Contextual Information

Zhang

Lei

Tan

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYFault localization is essential for solving the issue of software faults. Aiming at improving fault localization, this paper proposes a deep learning-based fault localization with contextual information. Specifically, our approach uses deep neural network to construct a suspiciousness evaluation model to evaluate the suspiciousness of a statement being faulty, and then leverages dynamic backward slicing to extract contextual information. The empirical results show that our approach significantly outperforms the state-of-the-art technique Dstar.

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A Survey on Software Fault Localization

Cited by 796 publications

References 310 publications

An assessment of operational coverage as both an adequacy and a selection criterion for operational profile based testing

An assessment of operational coverage as both an adequacy and a selection criterion for operational profile based testing

ORBS and the limits of static slicing

Deep Learning-Based Fault Localization with Contextual Information

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