Software fault localization using BP neural network based on function and branch coverage

Maru, Abha; Dutta, Arpita; Kumar, Kulamala Vinod; Mohapatra, Durga Prasad

doi:10.1007/s12065-019-00318-2

Cited by 20 publications

(16 citation statements)

References 19 publications

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“…Block size is determined by the compiler and it depends on the program size and structure. The standard size of a block is 5-7 statements [1]. Using statement coverage may result in ties of scores between the statements within the same block of a program.…”

Section: A Statistical Analysismentioning

confidence: 99%

“…• Other: When the rank of a faulty element is more than ten in the ranking list. Also, there is a special non-accumulating variant of Top-N categories, in which the cases where the bug fell into non-overlapping intervals of [1], (1, 3], (3,5], (5,10] or (10, ...] are counted. These categories show in how many cases an SBFL approach moves a bug into a better (for example, from (5,10] to (1,3]) or a worse (for example, from [1] to (1,3]) category.…”

Section: P Top-n Rankingmentioning

confidence: 99%

“…Thus, our modern life cannot be imagined without software. The extensive use of different software products in our day-to-day activities has led to a significant increase in their size and complexity [1]. As a result, the number and types of software faults have also increased.…”

Section: Introductionmentioning

confidence: 99%

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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: A Statistical Analysismentioning

confidence: 99%

Section: P Top-n Rankingmentioning

confidence: 99%

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A Survey of Challenges in Spectrum-Based Software Fault Localization

Sarhan

Beszedes

2022

IEEE Access

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“…Wang et al [23] analyzed the role of optimization techniques over the simple invariant-based method. Maru et al [24] proposed an effective approach for fault localization based on a back-propagation neural network that utilizes branch and function coverage information along with test case execution results to train the network.…”

Section: Literature Reviewmentioning

confidence: 99%

A Learning-Based Fault Localization Approach Using Subset of Likely and Dynamic Invariants

Shaikh¹,

Rizwan²,

Alghamdi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Fault localization is one of the main tasks of software debugging. Developers spend a lot of time, cost, and effort to locate the faults correctly manually. For reducing this effort, many automatic fault localization techniques have been proposed, which inputs test suites and outputs a sorted list of faulty entities of the program. For further enhancement in this area, we developed a system called SILearning, which is based on invariant analysis. It learns from some existing fixed bugs to locate faulty methods in the program. It combines machine-learned ranking, program invariant differences, and spectrum-based fault localization (SBFL). Using the execution of test cases and code coverage analysis, it obtains each method's invariant differences and the suspiciousness value based on the program spectral location and uses them as features for ranking the faulty methods. The experimental analysis of SILearning has been performed on the dataset of real fault which is extracted from the database Defects4J. The tools used in this research are Daikon and cobertura for detection of the invariants and code coverage, respectively. The results show that SILearning performs better when combined features are utilized and can successfully locate the faulty methods on average for 76.1, 90.4, 108.2, 123, and 143.5 at the top positions of 1, 2, 3, 4, and 5.

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“…The deep neural network models efficiently consider both features with an exceptional learning capability [8]. Various studies on fault localization incorporate something into the software to track the model's functionality and outcome [9,10]. The different fault localization techniques include programming constraints, program log reports, breakpoints, profiling.…”

Section: Introductionmentioning

confidence: 99%

Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

Alhumam

2021

Sensors

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The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.

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Software fault localization using BP neural network based on function and branch coverage

Cited by 20 publications

References 19 publications

A Survey of Challenges in Spectrum-Based Software Fault Localization

A Survey of Challenges in Spectrum-Based Software Fault Localization

A Learning-Based Fault Localization Approach Using Subset of Likely and Dynamic Invariants

Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

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