SMBFL: slice-based cost reduction of mutation-based fault localization

Chaleshtari, Nazanin Bayati; Parsa, Saeed

doi:10.1007/s10664-020-09845-4

Cited by 16 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Program elements are then ranked in descending order of their suspiciousness scores, based on which APR techniques can further be applied. Researchers have proposed a variety of fault localization techniques, such as spectrum-based [123,182], mutation-based [77,120], slicing-based [10,99] and learning-based [78,93] techniques. Among them, spectrum-based fault localization (SBFL) has been extensively utilized as a general mechanism to localize the statements that are likely to be faulty in the APR literature.…”

Section: Fault Localizationmentioning

confidence: 99%

A Survey of Learning-based Automated Program Repair

Zhang¹,

Fang²,

Ma³

et al. 2023

Preprint

View full text Add to dashboard Cite

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive opensource code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance.In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at https://github.com/QuanjunZhang/AwesomeLearningAPR. CCS Concepts: • Software and its engineering → Software testing and debugging; Software testing and debugging.

show abstract

Section: Fault Localizationmentioning

confidence: 99%

A Survey of Learning-based Automated Program Repair

Zhang¹,

Fang²,

Ma³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…IETCR [15] is introduced to reduce the execution of test cases by calculating the entropy change of tests and selecting a proportion of them according to the entropy values. SMBFL [17] is proposed to reduce the execution cost by examining only the statements in the dynamic slice of the program under test to reduce the number of statements to be mutated. Another limitation of MBFL is that impact information alone cannot distinguish many tied program elements so that more advanced program features should be further extracted.…”

Section: B Improvement Of Mutation-based Fault Localizationmentioning

confidence: 99%

Improving Mutation-Based Fault Localization via Mutant Categorization

Li¹,

Tadikonda²

2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Fault localization is one of the most important activities in software debugging. Among various fault localization techniques, mutation-based fault localization (MBFL) has been commonly studied with its promising performance. However, MBFL should be improved further by incorporating more useful program information. In this paper, we propose MuCatFL, a novel and lightweight technique for better MBFL via mutant categorization. In details, after executing the original test suite against all generated mutants, we categorize the mutants into two groups, positive mutants and negative mutants, to rank the tied program elements. We evaluate MuCatFL by performing an extensive study on 395 real software faults from the widely used benchmark Defects4J. The experimental results show that MuCatFL can significantly outperform MBFL techniques (e.g., localizing 138 faults within the Top-1 position on method-level, 43.75% more than traditional Metallaxis technique). We also investigate that only positive mutants can contribute to the effectiveness of MBFL. Our findings can also provide guidance for the strategies to reduce the execution cost of MBFL.

show abstract

“…We will discuss them in Section 9.2. [10], SBFL [11], [14]- [17], and the combination of slicing method and SBFL (S-SBFL) [21], [22] accordingly. For SBFL, each SPL system is considered as a nonconfigurable code.…”

Section: Datasetmentioning

confidence: 99%

“…In later studies [17], [27], [60], which are better, both of the passed and failed tests are utilized. Moreover, several studies [21], [22] have shown that the FL performance is improved by combining SBFL technique with slicing methods. In comparison, while these techniques are designed to localize bugs in a particular product and cannot be directly applied for variability bugs, VARCOP is specialized for variability bugs in SPL systems.…”

Section: Related Workmentioning

confidence: 99%

“…We conducted experiments to evaluate VARCOP in both single-bug and multiple-bug settings on a dataset of 1,570 versions (cases) containing variability bug(s) [18]. We compared VARCOP with the state-of-the-art approaches including (SBFL) [11], [14]- [17], the combination of the slicing method and SBFL (S-SBFL) [21], [22], and Arrieta et al [10] using 30 most popular SBFL ranking metrics [11], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Variability Fault Localization Approach for Software Product Lines

Nguyen,

Ngo,

Nguyen

et al. 2021

Preprint

View full text Add to dashboard Cite

Software fault localization is one of the most expensive, tedious, and time-consuming activities in program debugging. This activity becomes even much more challenging in Software Product Line (SPL) systems due to variability of failures. These unexpected behaviors are induced by variability faults which can only be exposed under some combinations of system features. The interaction among these features causes the failures of the system. Although localizing bugs in single-system engineering has been studied in-depth, variability fault localization in SPL systems still remains mostly unexplored. In this article, we present VARCOP, a novel and effective variability fault localization approach. For an SPL system failed by variability bugs, VARCOP isolates suspicious code statements by analyzing the overall test results of the sampled products and their source code. The isolated suspicious statements are the statements related to the interaction among the features which are necessary for the visibility of the bugs in the system. In VARCOP, the suspiciousness of each isolated statement is assessed based on both the overall test results of the products containing the statement as well as the detailed results of the test cases executed by the statement in these products. On a large public dataset of buggy SPL systems, our empirical evaluation shows that VARCOP significantly improves two state-of-the-art techniques by 33% and 50% in ranking the incorrect statements in the systems containing a single bug each. In about two-thirds of the cases, VARCOP correctly ranks the buggy statements at the top-3 positions in the ranked lists. For the cases containing multiple bugs, VARCOP outperforms the state-of-the-art approaches 2 times and 10 times in the proportion of bugs localized at the top-1 positions. Especially, in 22% and 65% of the buggy versions, VARCOP correctly ranks at least one bug in a system at the top-1 and top-5 positions.

show abstract

SMBFL: slice-based cost reduction of mutation-based fault localization

Cited by 16 publications

References 40 publications

A Survey of Learning-based Automated Program Repair

A Survey of Learning-based Automated Program Repair

Improving Mutation-Based Fault Localization via Mutant Categorization

A Variability Fault Localization Approach for Software Product Lines

Contact Info

Product

Resources

About