Fusion fault localizers

Lucia,; Lo, David; Xia, Xin

doi:10.1145/2642937.2642983

Cited by 43 publications

(26 citation statements)

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“…Spectrum-Based Bug Localization: Various spectrumbased bug localization approaches have been proposed in the literature [19,8,27,28,24,25,11,12,56,55,13,29]. These approaches analyze a program spectra which is a record of program elements that are executed in failed and successful executions, and generate a ranked list of program elements.…”

Section: Related Workmentioning

confidence: 99%

Information retrieval and spectrum based bug localization: better together

Oentaryo

2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

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112

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Debugging often takes much effort and resources. To help developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been proposed. IR-based techniques process textual information in bug reports, while spectrum-based techniques process program spectra (i.e., a record of which program elements are executed for each test case). Both eventually generate a ranked list of program elements that are likely to contain the bug. However, these techniques only consider one source of information, either bug reports or program spectra, which is not optimal. To deal with the limitation of existing techniques, in this work, we propose a new multi-modal technique that considers both bug reports and program spectra to localize bugs. Our approach adaptively creates a bug-specific model to map a particular bug to its possible location, and introduces a novel idea of suspicious words that are highly associated to a bug. We evaluate our approach on 157 real bugs from four software systems, and compare it with a state-of-the-art IR-based bug localization method, a state-of-the-art spectrum-based bug localization method, and three state-of-the-art multi-modal feature location methods that are adapted for bug localization. Experiments show that our approach can outperform the baselines by at least 47.62%, 31.48%, 27.78%, and 28.80% in terms of number of bugs successfully localized when a developer inspects 1, 5, and 10 program elements (i.e., Top 1, Top 5, and Top 10), and Mean Average Precision (MAP) respectively.

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Section: Related Workmentioning

confidence: 99%

Information retrieval and spectrum based bug localization: better together

Oentaryo

2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

Self Cite

112

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“…However, despite the results reported on small and mediumsize benchmarks, SBFL's practical effectiveness is controversial [14], [21]- [23]. As an example, Parnin and Orso [21] provide empirical evidence that SBFL techniques assume idealized behaviors from which real developers often divert.…”

Section: Introductionmentioning

confidence: 99%

A Critical Evaluation of Spectrum-Based Fault Localization Techniques on a Large-Scale Software System

Keller

Grunske

Heiden

et al. 2017

2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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Abstract-In the past, spectrum-based fault localization (SBFL) techniques have been developed to pinpoint a fault location in a program given a set of failing and successful test executions. Most of the algorithms use similarity coefficients and have only been evaluated on established but small benchmark programs from the Software-artifact Infrastructure Repository. In this paper, we evaluate the feasibility of applying 33 state-ofthe-art SBFL techniques to a large real-world project, namely ASPECTJ. From an initial set of 350 faulty version from the iBugs repository of ASPECTJ we manually classified 88 bugs where SBFL techniques are suitable. Notably, only 11 bugs of these bugs can be found after examining the 1000 most suspicious lines and on average 250 source code files need to be inspected per bug. Based on these results, the study showcases the limitations of SBFL on a larger program and points out areas for improvement.

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“…We experiment on programs from the SIR benchmark [2], which has been extensively used in research in software engineering, particularly by studies that propose new testing and fault localization approaches, e.g., [5], [18]. We reused SIR program specifications constructed by Le et al [9] and manually injected seeded bugs to the original programs.…”

Section: Preliminary Evaluationmentioning

confidence: 99%

Enhancing Automated Program Repair with Deductive Verification

et al. 2016

2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Abstract-Automated program repair (APR) is a challenging process of detecting bugs, localizing buggy code, generating fix candidates and validating the fixes. Effectiveness of program repair methods relies on the generated fix candidates, and the methods used to traverse the space of generated candidates to search for the best ones. Existing approaches generate fix candidates based on either syntactic searches over source code or semantic analysis of specification, e.g., test cases. In this paper, we propose to combine both syntactic and semantic fix candidates to enhance the search space of APR, and provide a function to effectively traverse the search space. We present an automated repair method based on structured specifications, deductive verification and genetic programming. Given a function with its specification, we utilize a modular verifier to detect bugs and localize both program statements and sub-formulas in the specification that relate to those bugs. While the former are identified as buggy code, the latter are transformed as semantic fix candidates. We additionally generate syntactic fix candidates via various mutation operators. Best candidates, which receives fewer warnings via a static verification, are selected for evolution though genetic programming until we find one satisfying the specification. Another interesting feature of our proposed approach is that we efficiently ensure the soundness of repaired code through modular (or compositional) verification. We implemented our proposal and tested it on C programs taken from the SIR benchmark that are seeded with bugs, achieving promising results.

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Fusion fault localizers

Cited by 43 publications

References 50 publications

Information retrieval and spectrum based bug localization: better together

Information retrieval and spectrum based bug localization: better together

A Critical Evaluation of Spectrum-Based Fault Localization Techniques on a Large-Scale Software System

Enhancing Automated Program Repair with Deductive Verification

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