Test scenario generation for feature-based context-oriented software systems

Martou, Pierre; Mens, Kim; Duhoux, Benoît; Legay, Axel

doi:10.1016/j.jss.2022.111570

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…The complexity of implementation and maintenance ICA algorithm [15] Higher coverage and speed Low stability Scenario-based Method [16] Higher scenario coverage Mainly for design testing, not for source-code testing Hyper Heuristic Model-based Testing [17] Higher program state coverage and lower execution time…”

Section: Methods Merits Demeritsmentioning

confidence: 99%

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

Arasteh,

Kiani

et al. 2024

Mathematics

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The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.

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Section: Methods Merits Demeritsmentioning

confidence: 99%

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

Arasteh,

Kiani

et al. 2024

Mathematics

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“…Software product line testing (SPLT) involves testing the shared components and individual product variants within an SPL [5]. It ensures the quality, compatibility, and correctness of both the common core assets and the unique features of each product.…”

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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Generalization of Autonomous Driving Simulation Scenarios with Monte Carlo Sampling

Liu,

Lv,

Zhao

2023

2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

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Test scenario generation for feature-based context-oriented software systems

Cited by 6 publications

References 28 publications

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

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

Generalization of Autonomous Driving Simulation Scenarios with Monte Carlo Sampling

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