Spectrum-Based Fault Localization via Enlarging Non-Fault Region to Improve Fault Absolute Ranking

Wang, Yong; Huang, Zhiqiu; Fang, Bingwu; Li, Yong

doi:10.1109/access.2018.2796849

Cited by 18 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Mean Average Precision (MAP): It represents the mean of the average precision of all faults [68]. We first define the precision of localization at each rank j: (16) where stmt is the number of code statements in the program, and isfault(j) is a Boolean function that represents whether the j-th statement is faulty or not. The higher the MAP value is, the more effective the fault localization is.…”

Section: ) Evaluation Metricsmentioning

confidence: 99%

A Framework for Improving Fault Localization Effectiveness Based on Fuzzy Expert System

2021

View full text Add to dashboard Cite

Many spectrum-based fault localization (SBFL) techniques have been proposed in order to improve debugging efficiency. These SBFL techniques were designed according to different underlying assumptions and then adopt different fault locator functions to evaluate the likelihood of each statement being faulty, called suspiciousness. So far no single SBFL technique claims that it can outperform all of the others under every scenario. That is, the effectiveness of fault localization may vary considerably by just adopting a single SBFL technique. Due to the aforementioned reasons, this study presents a framework for improving fault localization effectiveness by using a Fuzzy Expert System (FES) to integrate different SBFL techniques. In the presented framework, the outputs of several SBFL techniques will be put into the fuzzification and then transferred to fuzzy input sets. After undergoing the fuzzy inference based on the given fuzzy rules, the fuzzy input sets will be transferred to a fuzzy output set. Finally, the fuzzy set will be transferred to a crisp output (called a weighted suspiciousness value). The code statements will then be ranked according to their weighted suspiciousness values. In other words, no additional instrumentations and analyses on the source code and the test suite are necessary for our approach. Our experiment results indicate that our FES-based framework is effective at combining the SBFL techniques from different equivalent groups and achieves high effectiveness on the nine subject programs. It is also noted that in the literature, most of the approaches that combine multiple SBFL techniques are learning-based and they are suitable for long-term projects with sufficient historical data. Since our approach does not reference historical data for model training, it can be applied to new software projects. Thus, the application scenarios of our approach should be complementary to those of the state-of-the-art learning-based approaches.INDEX TERMS Software debugging, fault localization, fuzzy expert system.

show abstract

Section: ) Evaluation Metricsmentioning

confidence: 99%

A Framework for Improving Fault Localization Effectiveness Based on Fuzzy Expert System

2021

View full text Add to dashboard Cite

show abstract

“…EXAM = rank N umber of executable statements (5) The numerator in Equation 5 represents the rank of the faulty statement in the ranking list. And the denominator is the total number of statements that need to be checked.…”

Section: ) Mutant-test-pair Metricmentioning

confidence: 99%

IETCR: An Information Entropy Based Test Case Reduction Strategy for Mutation-Based Fault Localization

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Mutation-based fault localization (MBFL) is a recently proposed technique with the advantage of high fault localization accuracy. However, such a mutation analysis based technique is difficult to be accepted by industry due to its huge computational cost on mutation analysis. There are three ways to improve MBFL's efficiency, which are reducing the number of mutants, optimizing the mutants' execution process, and reducing the number of test cases. The former two ways have been mainly studied and shown promising results, but for the latter way, the related studies are limited since this kind of method will reduce the precision of MBFL. In this paper, we mainly focus on the latter way and propose an information entropy based test case reduction (IETCR) strategy for MBFL. In particular, we first calculate the entropy change of test cases and select a proportion of them according to their value. Then we use a reduced test suite to execute mutants. To show the effectiveness of the IETCR strategy, we choose six real-world programs with faulty versions. In terms of mutation reduction rate, we find MBFL with the IETCR strategy can reduce 56.3%∼88.3% cost while keeping almost the same fault localization accuracy when compared to the original MBFL without test case reduction. Moreover, we use Wilcoxon signed-rank test for statistical analysis, which shows that there is no statistically significant difference between MBFL with IETCR strategy and the original MBFL. INDEX TERMS Software fault localization, Mutation based fault localization, Information entropy, Test case reduction Recent studies [11], [12] have shown that mutation-based fault localization (MBFL) techniques outperform state-ofthe-art SBFL techniques in terms of the fault localization

show abstract

“…However, due to the nature of SBFL, it does not always rank the root causes at the top. Thus, the researchers in [38] proposed an SBFL technique that enlarges the nonfaulty region iteratively to narrow down the suspicious region and then ranks those components in the suspicious region using existing SBFL techniques to improve the absolute ranking of faulty elements.…”

Section: A Spectrum-based Fault Localizationmentioning

confidence: 99%

Improving Software Fault Localization by Combining Spectrum and Mutation

et al. 2020

View full text Add to dashboard Cite

The performance of software fault localization techniques is critical to software debugging and the reliability of software. Spectrum-based fault localization (SBFL) and mutation-based fault localization (MBFL) are the two most popular fault localization methods. However, the accuracies of the two methods are still limited. For example, only 10.63% of faults can be detected by inspecting the top 3 suspicious elements reported by Ochiai, which is a famous SBFL technique. Unfortunately, programmers only examine the first few suspicious elements before losing patience. Since the information used in SBFL and MBFL are quite different and complementary, this paper proposes a novel approach by combining spectrum and mutation to improve the fault localization accuracy. First, the faulty program is evaluated by using SBFL, and the potential faulty statements are ranked according to their suspiciousness. Then, mutants of the program are generated and executed by MBFL. Finally, the statements that are ranked in the top tied n by SBFL are evaluated and reranked according to their mutation scores. Experiments are carried on the Defects4J benchmark and the results reveal that the accuracy of the proposed approach outperforms those of the SBFL and MBFL techniques. In terms of the faults located by inspecting the top 1 suspicious elements, the SMFL techniques detect at least 2.36 times more faults than two SBFL techniques (DStar and Ochiai) and detect at least 1.86 times more faults than two MBFL techniques (MUSE and Metallaxis).

show abstract

Spectrum-Based Fault Localization via Enlarging Non-Fault Region to Improve Fault Absolute Ranking

Cited by 18 publications

References 29 publications

A Framework for Improving Fault Localization Effectiveness Based on Fuzzy Expert System

A Framework for Improving Fault Localization Effectiveness Based on Fuzzy Expert System

IETCR: An Information Entropy Based Test Case Reduction Strategy for Mutation-Based Fault Localization

Improving Software Fault Localization by Combining Spectrum and Mutation

Contact Info

Product

Resources

About