Enhanced Weighted Method for Test Case Prioritization in Regression Testing Using Unique Priority Value

Ammar, Asmaa; Baharom, Salmi; Ghani, Abdul Azim Abd; Din, Jafri

doi:10.1109/icissec.2016.7885851

Cited by 8 publications

(9 citation statements)

References 9 publications

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“…Elbaum et al [35] tested the modified modules and the dependent modules in stages. When faced with multiple optimization objectives, Marijan et al [36], and Ammar et al [37] prioritized test cases by adjusting weights of different optimization objectives. Strandberg et al [38] prioritized the test cases by assigning and merging priorities to the test case sequences by analyzing and combining execution time and its related factors.…”

Section: A Test Case Prioritizationmentioning

confidence: 99%

Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Yang

Pan

et al. 2021

IEEE Access

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Reinforcement learning (RL) has been applied to prioritizing test cases in Continuous Integration (CI) testing, where the reward plays a crucial role. It has been demonstrated that historical information-based reward function can improve the effectiveness of the test case prioritization (TCP). However, the inherent character of frequent iterations in CI can produce a considerable accumulation of historical information, which may decrease TCP efficiency and result in slow feedback. In this paper, the partial historical information is considered in the reward computation, where sliding window techniques are adopted to capture the possible efficient information. Firstly, the fixed-size sliding window is introduced to set a fixed length of recent historical information for each CI test. Then dynamic sliding window techniques are proposed, where the window size is continuously adaptive to each CI testing. Two methods are proposed, the test suite-based dynamic sliding window and the individual test case-based dynamic sliding window. The empirical studies are conducted on fourteen industrial-level programs, and the results reveal that under limited time, the sliding window-based reward function can effectively improve the TCP effect, where the NAPFD (Normalized Average Percentage of Faults Detected) and Recall of the dynamic sliding windows are better than that of the fixed-size sliding window. In particular, the individual test case-based dynamic sliding window approach can rank 74.18% failed test cases in the top 50% of the sorting sequence, with 1.35% improvement of NAPFD and 6.66 positions increased in TTF (Test to Fail).

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Section: A Test Case Prioritizationmentioning

confidence: 99%

Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Yang

Pan

et al. 2021

IEEE Access

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“…Random technique has been chosen since this study want to focus on how to define an indicator to prove that test cases with the same priority value have different fault coverage. The intention of this research is to avoid the random technique used to solve the issue of the same priority value, since it has been proven to be ineffective [21], [22]. The ordering of test suites for the Gomoku program, the Circular the Queue program, and the HashTable program uses the pure random technique.…”

Section: Experiments and Analysismentioning

confidence: 99%

Measuring the Efficiency of MFWA Technique for Prioritizing Event Sequences Test Cases

Ahmad¹

2019

IJATCSE

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The software development phase would frequently go through several changes and modifications. These are challenges that the tester of a system needs to face to ensure that the quality of the system aligns with the budget, resources, and time to deliver. Numerous techniques have been proposed to solve these problems and one of them is the test case prioritization (TCP) technique. The TCP technique is widely used for single event test cases. Thus, this paper would like to propose the Multifactor Weightage Approach (MFWA) using combinations of six factors to prioritize event sequence test cases. The percentage of test effort efficiency was used to measure the efficiency of the comparison technique. The results showed that the MFWA was more efficient compared to the random technique in terms of detecting faults earlier.

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“…Software testing is one of the expensive phases of the development process [2]. Software testing and debugging cost is over 50 percent of the whole development cost [3,4]. The cost of regression testing depends on the complexity of the application and the size of the test suite [5].…”

Section: Introductionmentioning

confidence: 99%

“…The fundamental challenge of software testing is to improve the order of the test cases for implementation to identify maximum faults in the given test suite [6]. To overcome this challenge, three common solutions are studied in the literature: test suite selection (TSS) [7,8], test suite minimization (TSM) [9,10] and test-suite prioritization (TSP) [1][2][3][4]6,[11][12][13][14][15]. TSP re-orders the test cases to identify more defects in the top few test cases.…”

Section: Introductionmentioning

confidence: 99%

“…There are many approaches used for the test case prioritization, such as data mining approaches [10,13], machine learning approaches [4,12,15,22], clustering approaches [5,26], multiple criteria-based approaches [1], weighted method for test case prioritization [3], and code coverage (CC) based TSP [7,9]. A similar study on the prioritization of test cases is done through data mining techniques called application navigation tree mining and interaction pattern mining [13].…”

Section: Introductionmentioning

confidence: 99%

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Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

et al. 2022

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Regression testing ensures that modified software code changes have not adversely affected existing code modules. The test suite size increases with modification to the software based on the end-user requirements. Regression testing executes the complete test suite after updates in the software. Re-execution of new test cases along with existing test cases is costly. The scientific community has proposed test suite prioritization techniques for selecting and minimizing the test suite to minimize the cost of regression testing. The test suite prioritization goal is to maximize fault detection with minimum test cases. Test suite minimization reduces the test suite size by deleting less critical test cases. In this study, we present a four-fold methodology of test suite prioritization based on reinforcement learning. First, the testers’ and users’ log datasets are prepared using the proposed interaction recording systems for the android application. Second, the proposed reinforcement learning model is used to predict the highest future reward sequence list from the data collected in the first step. Third, the proposed prioritization algorithm signifies the prioritized test suite. Lastly, the fault seeding approach is used to validate the results from software engineering experts. The proposed reinforcement learning-based test suite optimization model is evaluated through five case study applications. The performance evaluation results show that the proposed mechanism performs better than baseline approaches based on random and t-SANT approaches, proving its importance for regression testing.

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Enhanced Weighted Method for Test Case Prioritization in Regression Testing Using Unique Priority Value

Cited by 8 publications

References 9 publications

Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Measuring the Efficiency of MFWA Technique for Prioritizing Event Sequences Test Cases

Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

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