HSFal: Effective fault localization using hybrid spectrum of full slices and execution slices

Ju, Xiaolin; Jiang, Shujuan; Xiang, Chen; Wang, Xingya; Zhang, Yanmei; Cao, Heling

doi:10.1016/j.jss.2013.11.1109

Cited by 56 publications

(24 citation statements)

References 12 publications

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“…Despite the ability to exercise different aspects during a test execution, data-flow is rarely used to support SFL. Few initiatives use definition-use associations (DUAs) [6], [13] while others use program slicing [38], [39] or different types of data and control dependencies [13], [40], [41]. All these approaches demand a costly time overhead to collect data-flow spectra.…”

Section: Related Workmentioning

confidence: 99%

Evaluating data-flow coverage in spectrum-based fault localization

Ribeiro

Araujo

Chaim

et al. 2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: Debugging is a key task during the software development cycle. Spectrum-based Fault Localization (SFL) is a promising technique to improve and automate debugging. SFL techniques use control-flow spectra to pinpoint the most suspicious program elements. However, data-flow spectra provide more detailed information about the program execution, which may be useful for fault localization. Aims: We evaluate the effectiveness and efficiency of ten SFL ranking metrics using data-flow spectra. Method: We compare the performance of data-and control-flow spectra for SFL using 163 faults from 5 real-world open source programs, which contain from 468 to 4130 test cases. The data-and control-flow spectra types used in our evaluation are definition-use associations (DUAs) and lines, respectively. Results: Using data-flow spectra, up to 50% more faults are ranked in the top-15 positions compared to control-flow spectra. Also, most SFL ranking metrics present better effectiveness using data-flow to inspect up to the top-40 positions. The execution cost of data-flow spectra is higher than control-flow, taking from 22 seconds to less than 9 minutes. Dataflow has an average overhead of 353% for all programs, while the average overhead for control-flow is of 102%. Conclusions: The results suggest that SFL techniques can benefit from using data-flow spectra to classify faults in better positions, which may lead developers to inspect less code to find bugs. The execution cost to gather data-flow is higher compared to control-flow, but it is not prohibitive. Moreover, data-flow spectra also provide information about suspicious variables for fault localization, which may improve the developers' performance using SFL.

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Section: Related Workmentioning

confidence: 99%

Evaluating data-flow coverage in spectrum-based fault localization

Ribeiro

Araujo

Chaim

et al. 2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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“…Thirdly, as a substitute for heavyweight methods which cannot scale to large programs [30,36,37]. Fourthly, as a technique combined with other methods [21,24,[44][45][46][47][48][49][50]. In general, pfl may be used as a substitute for sbfl measures in all these applications.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Fault Localisation

Landsberg

Chockler

Kroening

2016

Hardware and Software: Verification and Testing

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Abstract. Efficient fault localisation is becoming increasingly important as software grows in size and complexity. In this paper we present a new formal framework, denoted probabilistic fault localisation (pfl), and compare it to the established framework of spectrum based fault localisation (sbfl). We formally prove that pfl satisfies some desirable properties which sbfl does not, empirically demonstrate that pfl is significantly more effective at finding faults than all known sbfl measures in large scale experimentation, and show pfl has comparable efficiency. Results show that the user investigates 37% more code (and finds a fault immediately in 27% fewer cases) when using the best performing sbfl measures, compared to the pfl framework. Furthermore, we show that it is theoretically impossible to design strictly rational sbfl measures that outperform pfl techniques on a large set of benchmarks.

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“…Mao et al [24] presented approximate dynamic backward slices to balance the size and precision of the slices and then statistically computed the suspiciousness. In the previous work [19], we presented a fault localization approach based on hybrid spectrum of full slices and execution slices and computed the suspiciousness of statements by a maximal formula we designed. Hofer et al [26] combined spectrum-based fault localization and slicing-hitting-set-computation to improve the ranking of the faults.…”

Section: Related Workmentioning

confidence: 99%

Applying Association Analysis to Dynamic Slicing Based Fault Localization

Cao

Jiang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYFault localization is a necessary process of locating faults in buggy programs. This paper proposes a novel approach using dynamic slicing and association analysis to improve the effectiveness of fault localization. Our approach utilizes dynamic slicing to generate a reduced candidate set to narrow the range of faults, and introduces association analysis to mine the relationship between the statements in the execution traces and the test results. In addition, we develop a prototype tool DSFL to implement our approach. Furthermore, we perform a set of empirical studies with 12 Java programs to evaluate the effectiveness of the proposed approach. The experimental results show that our approach is more effective than the compared approaches.

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HSFal: Effective fault localization using hybrid spectrum of full slices and execution slices

Cited by 56 publications

References 12 publications

Evaluating data-flow coverage in spectrum-based fault localization

Evaluating data-flow coverage in spectrum-based fault localization

Probabilistic Fault Localisation

Applying Association Analysis to Dynamic Slicing Based Fault Localization

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