Jaguar: A Spectrum-Based Fault Localization Tool for Real-World Software

Ribeiro, Henrique Lemos; Souza, Higor Amario de; Araujo, Roberto Paulo Andrioli de; Chaim, Marcos Lordello; Kon, Fábio

doi:10.1109/icst.2018.00048

Cited by 25 publications

(17 citation statements)

References 25 publications

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“…Data-flow based approaches share the belief data-flow analysis may improve the effectiveness to locate faults and obtain good results when compared to pure control-flow analysis. In this sense the suspiciousness scores of duas have been similarly computed as the original proposals to the control-flow coverage spectra [20][21][22]. Equation 2 presents the Tarantula heuristic applied to a dua.…”

Section: Data-flow Analysis For Fault Localizationmentioning

confidence: 99%

“…Ribeiro et al [20,21] propose JAGUAR, a tool to assist the debugging expert visually. The search for the faulty program element is guided by the suspiciousness ranking of duas: for each investigated dua, it considers that two elements were inspected to compute how many elements were checked until finding the faulty one.…”

Section: Data-flow Analysis For Fault Localizationmentioning

confidence: 99%

“…We utilized 112 versions with seeded faults from the C programs and 36 versions from the java one. We obtained the control and data-flow coverage spectra from the Siemens programs through manual code instrumentation, and the jsoup data were collected from the JAGUAR tool [20].…”

Section: Subject Programsmentioning

confidence: 99%

See 2 more Smart Citations

Data-flow-based evolutionary fault localization

Silva-Junior

Leitao-Junior

Dantas

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Fault localization is the activity of precisely indicating the faulty commands in a buggy program. It is known to be a highly costly and tedious process. Automating this process has been the goal of many studies, showing it to be a challenging problem. The coveragespectrum based approaches commonly apply heuristics grounded on the execution of control-flow components to calculate the odds of each program element to be the defective one. The present study aims to investigate another source of fault information by assessing how data-flow analysis are useful to compute suspiciousness scores; and how the combination of scores from different sources impacts fault localization. We present an approach to calculate the suspiciousness score for each program command by using the execution of data-flow components. Then we use an evolutionary algorithm to search sets of weights to combine heuristics from distinct sources of fault data (both control-flow and data-flow as well as a hybrid strategy). The approach was applied in programs with seeded faults and real faults and evaluated by using absolute metrics to asses its efficacy to locate faults. Furthermore, we introduce a new metric to investigate the dependence of tie-break strategies in building the ranking of suspicious commands. Data-flow based methods demonstrate high effectiveness but increase the need for tie-breaks, unlike the evolutionary hybrid method that keeps competitive the effectiveness and depends less on tie-break strategies. CCS CONCEPTS• Software and its engineering → Search-based software engineering; Software testing and debugging;

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Section: Data-flow Analysis For Fault Localizationmentioning

confidence: 99%

Section: Data-flow Analysis For Fault Localizationmentioning

confidence: 99%

See 1 more Smart Citation

Data-flow-based evolutionary fault localization

Silva-Junior

Leitao-Junior

Dantas

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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“…We used Jaguar [22] to generate the lists of suspicious DUAs and lines for each fault. Jaguar is an open source SFL tool that utilizes the instrumentation libraries of two testing tools: BA-DUA [21] to gather data-flow spectra and JaCoCo to gather control-flow spectra.…”

Section: B Data Collection Proceduresmentioning

confidence: 99%

“…We used the Jaguar SFL tool (JAva coveraGe faUlt locAlization Ranking) 3 [22] to compare data-and control-flow spectra in fault localization. Jaguar provides ranking lists for several ranking metrics.…”

Section: Introductionmentioning

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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Software Fault Localization: an Overview of Research, Techniques, and Tools

Wong

Gao²,

et al. 2023

Handbook of Software Fault Localization

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Jaguar: A Spectrum-Based Fault Localization Tool for Real-World Software

Cited by 25 publications

References 25 publications

Data-flow-based evolutionary fault localization

Data-flow-based evolutionary fault localization

Evaluating data-flow coverage in spectrum-based fault localization

Software Fault Localization: an Overview of Research, Techniques, and Tools

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