Improving Software Fault Localization by Combining Spectrum and Mutation

Cui, Zhanqi; Jia, Minghua; Chen, Xiang; Zheng, Lihua; Liu, Xiulei

doi:10.1109/access.2020.3025460

Cited by 23 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The trained model generates a suspicion list of bug positions based on all individual method results (Fejzer et al, 2021;Zou et al, 2019). Alternatively, the second approach narrows down the bug position range using one bug localization method, then applies another method to calculate suspicions and rank elements within the narrowed range (Cui, Jia, Chen, Zheng, & Liu, 2020).…”

Section: Related Work Of Combined Bug Localizationmentioning

confidence: 99%

PSC-SBFL: combining PSC with SBFL to improve software fault localization

Guo,

Ji,

et al. 2024

Preprint

View full text Add to dashboard Cite

Regression testing aims to confirm that program changes don't disrupt existing functionalities. Automated fault localization improves quality and efficiency of regression testing. Spectrum-Based Fault Localization (SBFL) is adept at identifying faults in program statements using test case execution data. However, SBFL overlooks faults due to structural anomalies and can't detect non-existent or redundant statements. This study introduces Program Structure Check (PSC) to address the issue. In regression testing, historical program versions provide valuable information for fault localization. PSC compares the structure of the program being tested with programs of historical versions to find structural differences, like missing code. This increases suspicion scores at these locations. Experimental findings show PSC detects over 90% of structural bugs, with over 76% ranked highest on the suspicion list. We combine PSC with SBFL, termed PSC-SBFL, and test it on public assemblies to assess bug location effects. Results indicate that adding PSC to SBFL enhances bug ranking by approximately 93% and reduces manual code checking by about 34% when all bugs are identified. Compared to another SBFL-based method, PSC-SBFL demonstrates superior bug localization. These findings underscore how combining PSC and SBFL algorithms enhances bug localization accuracy, expedites bug identification, and boosts software quality.

show abstract

Section: Related Work Of Combined Bug Localizationmentioning

confidence: 99%

PSC-SBFL: combining PSC with SBFL to improve software fault localization

Guo,

Ji,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It is remarkable that programmers pay more attention to the most suspicious elements in practice. As we mentioned before, in contrast, instead of checking the statements in the ranked list one by one, they exhibit some form of jumping between positions in the ranked list until a hypothesis about the cause of the failure is confirmed 43 . Furthermore, the top‐ranked positions are rarely skipped by programmers, so TOP‐1 is extremely vital for evaluating the effectiveness and accuracy of fault localization techniques.…”

Section: Experiments Subjectsmentioning

confidence: 99%

Spectrum‐based fault localization using empirical mode decomposition algorithm

Chen

Liu

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

Spectrum‐based fault localization (SBFL) is considered as the most popular lightweight fault localization method. However, pure SBFL is proved to be tedious and time‐consuming for programmers to detect faults. This is because the suspiciousness is duplicated and they usually involve only the first few suspicious elements in the debugging process before losing patience. For this reason, benefiting from abundant spectrum information created by SBFL, we propose a new spectral fault localization technique using empirical mode decomposition method (EMD) to improve the accuracy of automatic software debugging. To accomplish that, the faulty program is evaluated by SBFL, and then, the resultant suspiciousness scores are taken as signals and nonfaulty elements as noise. Next, EMD is employed to decompose the suspicious scores of SBFL to eliminate massively repeated ones. Hence, elements are reranked according to new scores, and the ranking list is reconstructed by enlarging the high‐performance range (checking 5% elements) and TOP‐5 region to detect more faults. The performance of EMD‐SBFL is tested and compared with pure SBFL with EXAM scores and Top‐n ranks both on Siemens programs with seeded faults and large‐sized Defects4j programs with real faults. The result reveals that EMD‐SBFL is significantly effective to locate nearly over doubled faults by only check Top‐1 and outperforms the state‐of‐the art SBFL techniques.

show abstract

“…Thus, to identify and locate elements more likely to be faulty. In SBFL, code coverage information (also called program spectra), which is obtained from executing a set of test cases with recording their results, is used, by a ranking formula, to calculate the probability of each program element (e.g., statement, block, or function) being faulty [13]. Code coverage provides information on which program element has been executed and which one has not during the execution of each test case, while test results are classified as passed or failed test cases.…”

Section: Background Of Sbflmentioning

confidence: 99%

A Survey of Challenges in Spectrum-Based Software Fault Localization

Sarhan

Beszedes

2022

IEEE Access

View full text Add to dashboard Cite

In software debugging, fault localization is the most difficult, expensive, tedious, and timeconsuming task, particularly for large-scale software systems. This is due to the fact that it requires significant human participation and it is difficult to automate its sub-tasks. Therefore, there is a high demand for automatic fault localization techniques that can help software engineers effectively find the locations of faults with minimal human intervention. This has led to the proposal of implementing different types of such techniques. However, Spectrum Based Fault Localization (SBFL) is considered amongst the most prominent techniques in this respect due to its efficiency and effectiveness. In SBFL, the probability of each program element (e.g., statement, block, or function) being faulty is calculated based on the results of executing test cases and their corresponding code coverage information. However, SBFL techniques are not yet widely adopted in the industry. The rationale behind this is that they pose a number of issues and their performance is affected by several influential factors. For example, the characteristics of bugs, target programs, test suites, and supporting tools make their effectiveness differ dramatically from one case to another. There are massive studies on SBFL that cover its usage, formulas, performance, etc. So far, no dedicated survey points out comprehensively the issues of SBFL. In this paper, various SBFL challenges and issues have been identified, categorized, and discussed alongside many directions. Also, the paper raises awareness of the works being achieved to address the identified issues and suggests some potential solutions too.INDEX TERMS Program spectra, spectrum based fault localization, software testing, challenges and issues, survey.

show abstract

Improving Software Fault Localization by Combining Spectrum and Mutation

Cited by 23 publications

References 38 publications

PSC-SBFL: combining PSC with SBFL to improve software fault localization

PSC-SBFL: combining PSC with SBFL to improve software fault localization

Spectrum‐based fault localization using empirical mode decomposition algorithm

A Survey of Challenges in Spectrum-Based Software Fault Localization

Contact Info

Product

Resources

About