Relation-based test case prioritization for regression testing

Chi, Jianlei; Qu, Yu; Zheng, Qinghua; Yang, Zijiang; Jin, Wuxia; Cui, Di; Liu, Ting

doi:10.1016/j.jss.2020.110539

Cited by 35 publications

(15 citation statements)

References 21 publications

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“…[134], [46], [48]-[51] 2 Clustering [10], [12], [42], [43], [53], [57], [60], [61], [69], [71], [79], [85], [21], [89], [90], [93], [101]- [103], [111], [114], [117], [118], [25], [126], [129], [130], [132], [133], [27], [31]- [35] 35…”

Section: Discussionmentioning

confidence: 99%

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

et al. 2021

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Software quality can be assured by passing the process of software testing. However, software testing process involve many phases which lead to more resources and time consumption. To reduce these downsides, one of the approaches is to adopt test case prioritization (TCP) where numerous works has indicated that TCP do improve the overall software testing performance. TCP does have several kinds of techniques which have their own strengths and weaknesses. As for this review paper, the main objective of this paper is to examine deeper on machine learning (ML) techniques based on research questions created. The research method for this paper was designed in parallel with the research questions. Consequently, 110 primary studies were selected where, 58 were journal articles, 50 were conference papers and 2 considered as others articles. For overall result, it can be said that ML techniques in TCP has trending in recent years yet some improvements are certainly welcomed. There are multiple ML techniques available, in which each technique has specified potential values, advantages, and limitation. It is notable that ML techniques has been considerably discussed in TCP approach for software testing.

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Section: Discussionmentioning

confidence: 99%

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

et al. 2021

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“…Each researcher executes the chosen technique based on specific analysis and considerations. Some of the algorithms used include Greedy and Additional Greedy for search-based TCP [37], [40], Firefly Algorithm [38], [78], Neural Network Classifier [44], Ant Colony Optimization [55], [70], FAST Algorithm [79], Support Vector Machine/SVM [80], Genetic [42], [59], [76], [81], Fuzzy Expert [77], Dynamic Programming [45], Recommender System [58], Clustering Technique [73], [82], [83], Particle Swarm Optimization [61], Natural Language Processing (NLP) [74], and Bat-inspired Algorithm [48].…”

Section: ) What Is the State Of The Art For Tcp Based On Tcp Approaches And Techniques? (Rq1)mentioning

confidence: 99%

A Systematic Literature Review on Regression Test Case Prioritization

Rahmani¹,

Ahmad²,

Jalil³

et al. 2021

IJACSA

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Test case prioritization (TCP) is deemed valid to improve testing efficiency, especially in regression testing, as retest all is costly. The TCP schedule the test case execution order to detect bugs faster. For such benefit, test case prioritization has been intensively studied. This paper reviews the development of TCP for regression testing with 48 papers from 2017 to 2020. In this paper, we present four critical surveys. First is the development of approaches and techniques in regression TCP studies, second is the identification of software under test (SUT) variations used in TCP studies, third is the trend of metrics used to measure the TCP studies effectiveness, and fourth is the stateof-the-art of requirements-based TCP. Furthermore, we discuss development opportunities and potential future directions on regression TCP. Our review provides evidence that TCP has increasing interests. We also discovered that requirement-based utilization would help to prepare test cases earlier to improve TCP effectiveness.

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“…Rothermel et al [19] gave a formal description of TCP in 2000 and conducted a series of empirical studies. Then a variety of TCP techniques are proposed [20], including coverage-based [21], modification-based [22], faultbased [23], requirement-based [24], and history-based TCP techniques [25], which are also used as criteria for TCP.…”

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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Relation-based test case prioritization for regression testing

Cited by 35 publications

References 21 publications

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

A Systematic Literature Review on Regression Test Case Prioritization

Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

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