Component-Based Software System Test Case Prioritization with Genetic Algorithm Decoding Technique Using Java Platform

Mahajan, Surendra; Joshi, Sarang; Khanaa, V.

doi:10.1109/iccubea.2015.169

Cited by 17 publications

(9 citation statements)

References 6 publications

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“…When changing software, it is useful to test if the new work damages old functionality. For this reason, testing and re-testing code is widely applied in both open-source projects and closedsource projects [15,16,17].…”

Section: Why Explore Test Suite Minimization?mentioning

confidence: 99%

Faster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization)

Chen¹,

Shen²

2021

Preprint

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Theorem provers has been used extensively in software engineering for software testing or verification. However, software is now so large and complex that additional architecture is needed to guide theorem provers as they try to generate test suites. The Snap test suite generator (introduced in this paper) combines the Z3 theorem prover with the following tactic: cluster some candidate tests, then search for valid tests by proposing small mutations to the cluster centroids. This technique effectively removes repeated structures in the tests since many repeated structures can be replaced with one centroid. In practice, Snap is remarkably effective. For 27 real-world programs with up to half a million variables, Snap found test suites which were 10 to 750 smaller times than those found by the prior state-of-the-art. Also, Snap ran orders of magnitude faster and (unlike prior work) generated 100% valid tests.

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Section: Why Explore Test Suite Minimization?mentioning

confidence: 99%

Faster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization)

Chen¹,

Shen²

2021

Preprint

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“…In second step, leftover TCs are given as input to GA for generating optimized sequence. In [15] authors have designed a Component-Based Software testing prioritization framework using GA with Java decoding technique to enhance the software quality by detecting faults at the early stage of software development. In [16], authors have developed an automation tool which is a hybrid combination of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm for TCP.…”

Section: Literature Surveymentioning

confidence: 99%

Fault Aware Test Case Prioritization in Regression Testing using Genetic Algorithm

Paygude¹

2020

IJETER

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In software testing practice, regression testing is significant type of testing, which is responsible for stability, quality, reliability and functionality of existing application even after doing the modifications in the application. Test case prioritization (TCP) is one of the proved effective techniques of regression testing that improves defect detection rate by scheduling execution of the test cases (TCs). Scheduling the execution of all TCs is very complex and time consuming task and thus needs to introduce the use of optimization algorithms. This paper implements a genetic optimization algorithm (GA) to improve the TCP technique by ordering the TCs with goal of maximum fault detection by minimal execution of TCs. The effectiveness of implemented GA optimization technique is measured using average percentage of fault detection (APFD) metric. We analyzed implemented GA approach to examine its effect on outcome by changing its vital parameters such as crossover, mutation and convergence criteria with the aim of increasing rate of fault detection. This experiment is evaluated on public dataset with more than 1000 TCs. We tend to compare our work with random search prioritization and hill climbing optimization algorithms. This carried out experimental outcome clearly depict that GA outperforms better than compared algorithms in solving TCP problem by improving the performance of regression testing.

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“…In [5], the necessity of Component-Based Software testing prioritization framework was evolved and proved, which exposed more severe bugs at an early level and improved software product deliverable quality employing Genetic Algorithm with java decoding technique. For this purpose, a set of prioritization keys was proposed.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…The next set of values has been obtained by running both the approaches for different number of generations, as given by Table 2. This has been achieved by setting the number of generations to [2], [3], [4] and [5]. Figures 5 and 6 represent the comparison bar graphs of APSC and execution time values respectively for both the approaches, whereas figures 7 and 8 show the comparison line graphs for the same.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Performance Analysis of Hybrid Approach Comprising Genetic Algorithm and Adaptive Approach on Test Case Prioritization

Walia¹,

Bajaj²

2016

IJCA

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Regression testing is an important domain of software testing, which attempts to verify all the fixes that had been introduced into the software throughout its development period by means of test suites. In spite of being exorbitant in terms of time and cost, it cannot be evaded. As a result, lot many techniques have been proposed in the past in order to minimize these expenses. One such technique is Test Case Prioritization, which works by scheduling the execution order of test cases with a goal of improving the fault detection rate. This paper introduces a hybrid approach to test case prioritization, by combining Genetic Algorithm and Adaptive approach. Initially, it applies the Adaptive approach for the prioritization of test cases. Further, the left over test cases are prioritized by applying the Genetic Algorithm. Finally, the outcomes obtained from the proposed approach are compared with those of Genetic Algorithm based on two parameters: execution time and average percentage of statement coverage (APSC) values. The evaluation results prove that the proposed approach performs better in terms of both the parameters.

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Component-Based Software System Test Case Prioritization with Genetic Algorithm Decoding Technique Using Java Platform

Cited by 17 publications

References 6 publications

Faster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization)

Faster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization)

Fault Aware Test Case Prioritization in Regression Testing using Genetic Algorithm

Performance Analysis of Hybrid Approach Comprising Genetic Algorithm and Adaptive Approach on Test Case Prioritization

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