Prioritizing JUnit Test Cases Without Coverage Information: An Optimization Heuristics Based Approach

Mukherjee, Rajendrani; Patnaik, Kiran Kumar

doi:10.1109/access.2019.2922387

Cited by 9 publications

(7 citation statements)

References 39 publications

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“…A supportive tool sOrTES is introduced to measure independence and ranking of integration test cases based on execution time and requirements coverage [11]. An approach for ordering JUnit test cases is proposed by using optimization heuristics including the Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and Multi-Objective Genetic Algorithm (MOGA) [12]. Ant Colony Optimization (ACO) based techniques are used to solve coverage-based TCP problems and are better than Genetic Algorithm based techniques [13].…”

Section: Related Workmentioning

confidence: 99%

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Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Ahmed¹,

Majeed²,

Khan³

2023

Computers, Materials &Amp; Continua

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Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results. In this paper, two value-based TCP techniques have been introduced using Genetic Algorithm (GA) including Value-Cognizant Fault Detection-Based TCP (VCFDB-TCP) and Value-Cognizant Requirements Coverage-Based TCP (VCRCB-TCP). Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value (APFD v ) and Average Percentage of Requirements Coverage per value (APRC v ). Two case studies are performed to validate proposed techniques and performance evaluation metrics. The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order (OO), Reverse Order (REV-O), Random Order (RO), and Greedy algorithm.

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Section: Related Workmentioning

confidence: 99%

“…v value to fitmax1 and assign its permutation to parent A12 Assign the second-best APFD v value to fitmax2 and assign its permutation to parent B13 Update BestAPFD v and its permutation with fitmax1 and parent A 14Perform order crossover on permutation with BestAPFD…”

mentioning

confidence: 99%

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Ahmed¹,

Majeed²,

Khan³

2023

Computers, Materials &Amp; Continua

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“…Concentrating on the studies that suggest prioritizing test cases in a CI context based on testing characteristics [29][30][31] are very few, and pointing out their inconsistencies at this stage would be irrelevant. The distinction between this paper and previous work is that this study concentrated on the most extensive levels of testing, namely unit and integration, for TCP, which no previous study has done.…”

Section: Figurementioning

confidence: 99%

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Gupta¹,

Mahapatra²

2023

Computers, Materials &Amp; Continua

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Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product. Due to resource constraints, when software is subjected to modifications, the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy. One such strategy is test case prioritization (TCP). Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest. Nonetheless, singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches. Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile, this study has introduced a memetics-inspired methodology for TCP. The proposed structure is first embedded with diverse parameters, and then traditional steps of the shuffled-frog-leaping approach (SFLA) are followed to prioritize the test cases at unit and integration levels. On 5 standard test functions, a comparative analysis is conducted between the established algorithms and the proposed approach, where the latter enhances the coverage rate and fault detection of re-ordered test sets. Investigation results related to the mean average percentage of fault detection (APFD) confirmed that the proposed approach exceeds the memetic, basic multi-walk, PSO, and optimized multi-walk by 21.7%, 13.99%, 12.24%, and 11.51%, respectively.

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“…Several TCP techniques have been presented to enhance fault detection rates, with multi-coverage weighting techniques exhibiting promising performance. However, there has been relatively less focus on TCP techniques for OOP, particularly in Java [27] to [29].…”

Section: Related Workmentioning

confidence: 99%

Diversity-based Test Case Prioritization Technique to Improve Faults Detection Rate

Nuh¹,

Koh²,

Baharom³

et al. 2023

IJACSA

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Regression testing is an important task in software development, but it is often associated with high costs and increased project expenses. To address this challenge, prioritizing test cases during test execution is essential as it aims to swiftly identify the hidden faults in the software. In the literature, several techniques for test case prioritization (TCP) have been proposed and evaluated. However, existing weightbased TCP techniques often overlook the true diversity coverage of test cases, resulting in the use of average-based weighting practices and a lack of systematic calculation for test case weights. Our research revolves around prioritizing test cases by considering multiple code coverage criteria. The study presents a novel diversity technique that calculates a diversity coverage score for each test case. This score serves as a weight to effectively rank the test cases. To evaluate the proposed technique, an experiment was conducted using five open-source programs and measured its performance in terms of the average percentage of fault detection (APFD). A comparison was made against an existing technique. The results revealed that the proposed technique significantly improved the fault detection rate compared to the existing approach. It is worth noting that this study is the first of its kind to incorporate the true diversity score of test cases into the TCP process. The findings of our research make valuable contributions to the field of regression testing by enhancing the effectiveness of the testing process through the utilization of diversity-based weighting techniques.

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Prioritizing JUnit Test Cases Without Coverage Information: An Optimization Heuristics Based Approach

Cited by 9 publications

References 39 publications

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Diversity-based Test Case Prioritization Technique to Improve Faults Detection Rate

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