IETCR: An Information Entropy Based Test Case Reduction Strategy for Mutation-Based Fault Localization

Wang, Haifeng; Du, Bowen; He, Jie; Liu, Yong; Chen, Xiang

doi:10.1109/access.2020.3004145

Cited by 27 publications

(15 citation statements)

References 67 publications

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“…Xiaoan Bao et al described an improved adaptive genetic algorithm (IAGA) in [16] to generate the test cases by preserving population diversity. Haifeng Wang et al designed an information entropy-based test case reduction (IETCR) scheme in [17] Pedro Delgado-Pé rez and Inmaculada Medina-Bulo designed an Evolutionary Mutation Testing (EMT) in [18] for reducing the number of test suites. But the selected test suites were not efficient to cover the more faults.…”

Section: Related Workmentioning

confidence: 99%

Hybridization of Buffalo and Truncative Cyclic Gene Deep Neural Network-based Test Suite Optimization for Software Testing

Ramasundaram¹,

Sangeetha²

2022

IJMECS

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Software testing is the significant part of the software development process to guarantee software quality with testing a program for discovering the software bugs. But, the software testing has a long execution time by using huge number of test suites in the software development process. In order to overcome the issue, a novel technique called Hybridized Buffalo and Truncation Cyclic Gene Optimization-based Densely Connected Deep Neural Network (HBTCGO-DCDNN) introduced to improve the software testing accuracy with minimal time consumption. At first, the numbers of test cases are given to the input layer of the deep neural network layer. In the first hidden layer, the test suite generation process is carried out by applying the improved buffalo optimization technique with different objective functions namely time and cost. The improved buffalo optimization selects optimal test cases and generates the test suites. After the generation, the redundant test cases from the test suite are eliminated in the reduction process in the second hidden layer. The Truncative Cyclic Uniformed Gene Optimization technique is applied for the test suite reduction process based on thefault coverage rate. Finally, the reduced test suites are obtained at the output layer of the deep neural network The experimental evaluation of the HBTCGO-DCDNN and existing methods are discussed using the test suite generation time, test suite reduction rate as well as fault coverage rate. The comparative results of proposed HBTCGO-DCDNN technique provide lesser the generation time by 48% and higher test suit reduction rate by 19% as well as fault coverage rate 18% than the other well-known methods.

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Section: Related Workmentioning

confidence: 99%

Hybridization of Buffalo and Truncative Cyclic Gene Deep Neural Network-based Test Suite Optimization for Software Testing

Ramasundaram¹,

Sangeetha²

2022

IJMECS

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“…Some challenges do arise due to the frequency with which software testing is performed in conjunction to software development. With every development it is must to perform one round of testing including sanity testing but sometimes this pattern does not get followed due to some reason like either the resource availability is not spontaneous or often executing all the test cases is quite time-consuming process [1][2][3][4]. Thus, it is being devised that some test case reduction applications can be explored for enhancement of the overall process of software testing.…”

Section: Introductionmentioning

confidence: 99%

“…Some optimization strategies are required to introduce for solving the amplification of number of test cases with evolution of system software simultaneously [5,[8][9][10]. Therefore, many optimization techniques are proposed in order to reduce the efforts put for regression testing by selecting few test cases from the existing test-suite [1,4,[11][12][13][14]. Test case selection is one of the major roles in regression testing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Geetha¹,

Sankar²

2022

Computer Systems Science and Engineering

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Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.

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“…Model-based testing (MBT) [15], classification, clustering, and the advantage of high fault localization accuracy [16]. However, the cost of the mutation process remains high.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Mutation Testing Challenges to Fixing Faults

Arnomo¹,

Ibrahim²,

Putri³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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One of the challenges of mutation testing is fixing faults. In the debugging phase, all live mutants were repaired. Programs need high mutation scores to be declared reliable program codes. Each mutation test can allow the identification of multiple mutants. This is what confuses the faults fixing process. The objective of this research is to get the shortest route so that it can help in sorting the mutant types during application improvement after testing. The optimization is needed considering the number of mutants in each mutation testing. The problems related to optimization are very complex. It takes a suitable method to find the shortest path by paying attention to each point. There are 30 projects chosen randomly. The operator mutations that are often killed when testing mutations are AOIU and COI. The proposed optimization for mutant repair sequence is the ant colony system (ACS). The route selection using the Ant Colony System algorithm resulted in route optimization of 1.528254. Meanwhile, if the genetic algorithm is used, the score is 1.767643. Optimization results are very helpful for developers in improving code in mutation testing. Research states the best order for handling mutants using ACS. This research can be further developed with the addition of class-level mutant cases which are produced using class mutation operators. Class mutation operators have different characteristics from traditional mutation operators. In particular, it requires changes to the program structure, such as the definition of class variables.

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IETCR: An Information Entropy Based Test Case Reduction Strategy for Mutation-Based Fault Localization

Cited by 27 publications

References 67 publications

Hybridization of Buffalo and Truncative Cyclic Gene Deep Neural Network-based Test Suite Optimization for Software Testing

Hybridization of Buffalo and Truncative Cyclic Gene Deep Neural Network-based Test Suite Optimization for Software Testing

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Optimization of Mutation Testing Challenges to Fixing Faults

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