Test Case Prioritization for Service-Oriented Workflow Applications: A Perspective of Modification Impact Analysis

Wang, Hongda; Yang, Ming; Li-hua, Jiang; Xing, Jianchun; Yang, Qiliang; Yan, Fuyong

doi:10.1109/access.2020.2998545

Cited by 7 publications

(4 citation statements)

References 36 publications

(56 reference statements)

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“…Also, PSO prioritization helps in improving the test cases execution time by considering selected set of test cases and eliminating the irrelevant set of test cases in turn helping by improving the code coverage information as well [18,20,29,30]. One more interesting feature of PSO algorithm is that it helps in generating improvised APFD results when compared with random ordering [31][32][33]. PSO algorithm has a capability of improving fault detection but does not focus much on the PSO performance when compared with the SI algorithms in TSR [7,14,20,26].…”

Section: Related Workmentioning

confidence: 99%

“…1 to 6, proposed approach decreases no of test cases in test suite and increases coverage, cost, fault rate and overall efficiency of testing process. By considering the result of different application, proposed approach is more efficient for solving test suite reduction problem [4,15,16,32,34].…”

Section: If Prev_pbest>=curr_pbest Then Pbest=prev_pbest Else Pbest=c...mentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: If Prev_pbest>=curr_pbest Then Pbest=prev_pbest Else Pbest=c...mentioning

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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“…The SSH framework was developed based on Java and it is an open-source framework for web applications. Web service technology is coordinated by service-oriented workflow applications to provide different functions [43]. The application of web technology to support remote access functions facilitates dynamic collaboration between the testers [44].…”

Section: System Design and Implementationmentioning

confidence: 99%

Standardization Workflow Technology of Software Testing Processes and its Application to SRGM on RSA Timing Attack Tasks

Han

Zhang

et al. 2022

IEEE Access

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Due to changing software requirements and increasing system complexity, which put forward new challenges to the related testing processes to ensure software trustworthiness. Software testing is an important means to improve software quality, and standardized software testing process has become an effective measure to ensure software testing quality. In order to solve the problem of software quality decline caused by non-standard behaviors in software testing processes, this paper proposes a standardization workflow technology to construct the management system of software testing processes, and applies the workflow management system to the software testing of RSA timing attack tasks. For the numerical experimental setup, two software fault detection time-domain datasets are considered, i.e., datasets generated from the RSA timing attack program in functional testing processes and workflow verification system used to manage the RSA timing attack processes in non-functional testing processes. Then, software reliability is evaluated by employing multiple heterogeneous software reliability growth models (SRGMs) with corresponding assumed conditions, parameter information and reliability prediction methods. Furthermore, we compare the fitting power and predictive performance of multiple SRGMs in terms of the model evaluation criteria, which helps testers select a better SRGM from the model evaluation results for reliability analysis. The experimental results show that the proposed standardization workflow technology can reasonably regulate the software testing processes, making the fault data input into SRGMs more reliable and reliability analysis results more credible.INDEX TERMS Software testing, standardization workflow technology, workflow management system, RSA timing attack tasks, software reliability growth model.

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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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Test Case Prioritization for Service-Oriented Workflow Applications: A Perspective of Modification Impact Analysis

Cited by 7 publications

References 36 publications

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

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

Standardization Workflow Technology of Software Testing Processes and its Application to SRGM on RSA Timing Attack Tasks

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

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