A Novel technique for test case minimization in object oriented testing

Taneja, Divya; Singh, Rajvir; Singh, Ajmer; Malik, Himanshu

doi:10.1016/j.procs.2020.03.274

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…In [7], Divya Taneja et al presented a Linear Regression technique for the minimization of test cases. The deigned technique efficiently decreases the execution time for reducing the test cases.…”

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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“…SPL testing may be completed more rapidly with this method. Package level security metrics were used by Tanejaa et al [15] to demonstrate how to assess the probability of errors in software modules that utilize objects. It improves FEP and cuts down on execution time.…”

Section: Introductionmentioning

confidence: 99%

An efficient test suit reduction methodology for regression testing

Gupta,

Choudhary

2024

IJEECS

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<span lang="EN-US">This paper's goal is to provide a more effective algorithm for reducing the amount of test cases in a test suit during the regression testing phase. This algorithm divide the entire test suit into equivalence classes in first step and then apply boundary value coverage to select test case out of repeated test cases which has same importance in test suit. This algorithm is based on the concept that before selecting best test cases out of repeated test case in test suit to prepare reduced test suit we can divide all test cases in number of equivalence classes so number of test case under consideration reduced by great extend. This paper proposed a method of experimentation involving test cases from different software application areas; minimization algorithms and the maths and algorithms of minimization algorithms in details. Test case techniques are equivalence portioning and boundary value analysis. Along with this concept, I also discuss a case study to verify and check new algorithm for its efficiency, for that I apply my algorithm on one of the program or group of program. This complete proposed methodology shall be applied to different software applications belonging to soft computing, Engineering software, Financial Software, Cloud Applications, Business Applications, AI and Machine Learning, Data Analytics are being identified. This selection has been made keeping in mind the trends, industrial importance, economic values and research challenges. Minimization in test cases would lead to lesser testing effort and desirable test completion.</span>

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“…Methods for RT include minimization, selection and prioritization. Minimization eliminates redundant TCs [6], selection selects the most essential TCs [7] and TCP methods re-order a test suite to identify the best order of TCs, enhancing objectives like early failure recognition [8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Learning Based Test Case Prioritization Using Deep Reinforcement Learning

2024

IJIES

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Continuous integration (CI) testing is crucial in modern software engineering and test case prioritization (TCP) techniques improve regression testing (RT) by prioritizing test cases (TCs). Various model has been developed to improve TCs failure prediction and prioritization in CI environments. But, prioritizing the TCs on large test suites without loss of information is a major challenging task. To address this, deep reinforcement prioritizer (DeepRP) model is proposed to improve prioritization in TCP on large test suites. This model employs deep reinforcement learning (DRL) model to learn more test case features, such as changes in source code, version control and code coverage. Also, it enhances self-optimization and adaptive ability for TCP. DRL training employs a deep neural network (DNN) structure to approximate various RL functions like value operation, Q function, transformation system and reward function. An RL system called Q-Learning which determines the appropriate action for an agent based on their action-value role. The DeepRP model uses test case features as input data and the priority of the test case as output. The action includes categorising TCs based on given scores, updating evaluations, calculating reward, and storing the chosen score in a temporary vector among the operations. The reward is computed based on the difference among the specified and ideal rankings for improved TCP on large test suites. The actions include sorting TCs based on assigned scores, updating observations, computing a reward and preserving the selected score in a temporary vector. The reward is calculated based on the distance between the assigned and optimal ranks for better TCP on large test suites. Finally, experimental results show DeepRP significantly achieves RMSE values of 0.09, 0.11 and 0.10 on paint control, IOF/ROL and GSDTSR datasets which is lesser than existing models algorithms like Deepgini, Hansie, DeepOrder, LogTCP and RL-TCP models.

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A Novel technique for test case minimization in object oriented testing

Cited by 12 publications

References 13 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

An efficient test suit reduction methodology for regression testing

An Improved Deep Learning Based Test Case Prioritization Using Deep Reinforcement Learning

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