Detecting false-passing products and mitigating their impact on variability fault localization in software product lines

Nguyen, Thu-Trang; Ngo, Kien-Tuan; Nguyen, Son; Vo, Hieu Dinh

doi:10.1016/j.infsof.2022.107080

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…This type of testing addresses the challenges posed by varying configurations, shared components, and differing features, while maintaining overall product line quality [6]. Similar to testing in non-configurable code, testing in SPL experiences the coincidental correctness phenomena, which makes it more challenging to detect errors in these systems [7]. However, the testing of a single software system is a highly difficult and expensive stage of the software development process, according to the author [8].…”

Section: Introductionmentioning

confidence: 99%

The Hybrid Jaro-Winkler and Manhattan Distance using Dissimilarity Measure for Test Case Prioritization Approach

Shareef,

Sulaiman,

Basari

2023

IJACSA

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Software product line (SPL) is a concept that has revolutionized the software development industry. It refers to a set of related software products that are developed from a common set of core assets but can be customized to meet specific customer requirements. Integrating SPL techniques into test case prioritization (TCP) can greatly enhance its effectiveness. By considering variability across different products within an SPL, it becomes possible to prioritize test cases based on their relevance to specific product configurations. However, the concept itself still has certain issues, such as in finding the highest rate of early failure detection. Various solutions have been proposed to mitigate this problem, among them is to improve the calculation of string distance using hybrid technique to achieve a high degree for similarity. Dissimilarity-based Technique (DBP) is the basis for our ranking method. The objective is to identify further weaknesses in the product lines as well as the differences between the experiment and real-world applications. Our focus is to enhance hybrid techniques that produce the highest rate of early failure detection. In this paper, early fault detection is selected as the performance goal. In order to choose the optimal methods for DBP for TCP, a comparison between several string distance measures was conducted. This study proposed hybrid techniques that combined Jaro-Winkler and Manhattan string distance namely New Enhanced Hybrid Technique 1 (NEHT1), New Enhanced Hybrid Technique 2 (NEHT2) and New Enhanced Hybrid Technique 3 (NEHT3). The case study was generated using the PLEDGE tool based on a Feature Model (FM). Six test cases were used in the experiment. Result shows the effectiveness of the combination where it achieved higher degree of similarity for T1 vs. T4, T2 vs. T3, T2 vs. T6, and T3 vs. T6, as well as perfect degree of similarity for NEHT1 (100.00%). The result proves that the combination of both techniques improve SPL testing effectiveness compared to existing techniques.

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Section: Introductionmentioning

confidence: 99%

The Hybrid Jaro-Winkler and Manhattan Distance using Dissimilarity Measure for Test Case Prioritization Approach

Shareef,

Sulaiman,

Basari

2023

IJACSA

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Identifying Coincidental Correct Test Cases with Multiple Features Extraction for Fault Localization

Tian

Yang

et al. 2023

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

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Clue: A Clustering-Based Test Reduction Approach for Software Product Lines

Vo,

Nguyen

2024

JCC

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Nowadays, organizations have increasingly turned to software product lines (SPLs)/configurable systems to meet diverse user needs and market demands. SPLs offer configurability and scalability but pose a formidable challenge in testing. Assuring the quality of variants across numerous configurations demands innovative testing strategies that balance effectiveness and efficiency. To improve testing productivity, test reduction techniques have been widely employed in non-configurable code. However, test reduction for SPL systems remains mostly unexplored. This paper introduces CLUE, a novel test reduction approach to enhance testing productivity in SPLs. Our idea is that to effectively and efficiently reveal failures, tests should be distinctive and cover diverse behaviors of the system. Meanwhile, similar tests covering the same/similar behaviors of an SPL system do not need to be redundantly executed multiple times. CLUE clusters the system’s tests into distinctive groups containing similar tests. Within each cluster, tests are prioritized based on the number of feature interactions, a common root cause of defects in SPLs. CLUE continually selects and executes the top-prioritized test in each cluster until a bug is revealed or the allocated effort budget is exhausted. To evaluate CLUE, we conducted several experiments on a dataset of six widely-used SPL systems. The results show that CLUE enables developers to detect defects earlier, requiring up to 88% less effort than existing approaches. Additionally, using only 50% of the original test suites, CLUE can identify most of the bugs in the buggy SPL systems while maintaining fault localization performance.

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Detecting false-passing products and mitigating their impact on variability fault localization in software product lines

Cited by 3 publications

References 38 publications

The Hybrid Jaro-Winkler and Manhattan Distance using Dissimilarity Measure for Test Case Prioritization Approach

The Hybrid Jaro-Winkler and Manhattan Distance using Dissimilarity Measure for Test Case Prioritization Approach

Identifying Coincidental Correct Test Cases with Multiple Features Extraction for Fault Localization

Clue: A Clustering-Based Test Reduction Approach for Software Product Lines

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