FATOC: Bug Isolation Based Multi-Fault Localization by Using OPTICS Clustering

Wu, Yonghao; Li, Zheng; Liu, Yong; Chen, Xiang

doi:10.1007/s11390-020-0549-4

Cited by 17 publications

(6 citation statements)

References 47 publications

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“…In other words, each test cluster represents a different bug. Then, the failed test cases in each cluster combined with all passed test cases are used to localize only a single fault as in [63]- [65].…”

Section: G Single and Multiple Bugsmentioning

confidence: 99%

A Survey of Challenges in Spectrum-Based Software Fault Localization

Sarhan

Beszedes

2022

IEEE Access

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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.

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Section: G Single and Multiple Bugsmentioning

confidence: 99%

A Survey of Challenges in Spectrum-Based Software Fault Localization

Sarhan

Beszedes

2022

IEEE Access

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“…Considering that the interactive behaviors among software entities implied some fault patterns, Zhao et al [18] introduced the fault influence of interactive entities and developed a novel synthetical fault localization approach based on the software network. Wu et al [19] adopted OPTICS clustering to group failed test cases, the failed test cases in this cluster, with all passed test cases to locate a single-bug.…”

Section: Spectrum-based Fault Localization (Sbfl)mentioning

confidence: 99%

An Innovative Framework for Coincidental Correctness Impacting on Fault Localization

Cao

Sun

2022

Symmetry

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An important research aspect of Spectrum-Based Fault Localization (SBFL) is the influence factors of the effectiveness of suspiciousness formulas from the perspective of symmetry. Coincidental correctness is one of the most important factors impacting the effectiveness of suspiciousness formulas. The influence of fault localization by coincidental correctness has attracted a large amount of research in the perspective of empirical study; however, it can hardly be considered as sufficiently comprehensive when there are a large number of the symmetrical suspiciousness formulas. Therefore, we first develop an innovative theoretical framework with function derivation investigating suspiciousness formulas impacted by coincidental correctness. We define three types of relations between formulas affected by coincidental correctness: namely, improved type, invariant type and uncertain type. We investigated 30 suspiciousness formulas using this framework and group them into three categories. Furthermore, we conduct an empirical study to verify the effectiveness of SBFL affected by coincidental correctness on four relatively large C programs. We proved that coincidental correctness has a positive effect on 23 out of these 30 formulas, no effect on 5 of them, and the effect on the remaining 2 of them depend on certain conditions. The experimental results show that the effectiveness of some suspiciousness formulas can be enhanced and that of some suspiciousness formulas remain unchanged.

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“…Many researchers have attempted to employ the clustering technique to divide failed test cases [3,12,[25][26][27]. Ideally, failures caused by the same fault should be grouped into a cluster, then the failed test cases in a cluster are combined with all successful test cases to form a fault-focused TS targeting a specific fault, as defined in Formula 1 and Formula 2.…”

Section: Why Clustering?mentioning

confidence: 99%

“…Based on one-fault-ata-time via OPTICS (Ordering Points To Identify the Clustering Structure) clustering, Wu et al proposed to 1) divide failed test cases in each iteration and calculate the density of each cluster, 2) combine the failed test cases in the cluster with the highest density value with all successful test cases to form a new test suite, and 3) localize a single fault based on the ranking list produced by the new test suite, iterating these steps until all bugs are fixed. Based on their findings, they further concluded that using the clustering algorithm with the highest accuracy can achieve the best performance of multi-fault localization [25]. Inspired by the multiple-fault-at-a-time strategy, Zheng et al converted fault localization tasks into search problems and proposed a fast software multi-fault localization framework using genetic algorithms [71].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging

Song¹,

Xie²,

Quan-ming³

et al. 2022

Preprint

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The clustering technique has attracted a lot of attention as a promising strategy for parallel debugging in multi-fault scenarios, this heuristic approach (i.e., failure indexing or fault isolation) enables developers to perform multiple debugging tasks simultaneously through dividing failed test cases into several disjoint groups. When using statement ranking representation to model failures for better clustering, several factors influence clustering effectiveness, including the risk evaluation formula (REF), the number of faults (NOF), the fault type (FT), and the number of successful test cases paired with one individual failed test case (NSP1F). In this paper, we present the first comprehensive empirical study of how these four factors influence clustering effectiveness. We conduct extensive controlled experiments on 1060 faulty versions of 228 simulated faults and 141 real faults, and the results reveal that: 1) GP19 is highly competitive across all REFs, 2) clustering effectiveness decreases as NOF increases, 3) higher clustering effectiveness is easier to achieve when a program contains only predicate faults, and 4) clustering effectiveness remains when the scale of NSP1F is reduced to 20%.

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FATOC: Bug Isolation Based Multi-Fault Localization by Using OPTICS Clustering

Cited by 17 publications

References 47 publications

A Survey of Challenges in Spectrum-Based Software Fault Localization

A Survey of Challenges in Spectrum-Based Software Fault Localization

An Innovative Framework for Coincidental Correctness Impacting on Fault Localization

A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging

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