Test Suite Reduction via Evolutionary Clustering

Chun-yan, Xia; Zhang, Yan; Hui, Zhanwei

doi:10.1109/access.2021.3058301

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…The Manhattan distance indicates the sum of the absolute axis distances of two points on a standard coordinate system. The formula is shown in Equation (2).…”

Section: Similarity Metricmentioning

confidence: 99%

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A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

Chen

Cai

et al. 2023

J Software Evolu Process

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Regression testing is an essential and expensive process in software testing. However, there may be insufficient resources for the execution of all test cases during regression testing. Test case prioritization (TCP) techniques improve the efficiency of regression testing by adjusting the test case execution sequence. Traditional TCP techniques usually rely on the historical execution information of the software under test for more efficient results. String distance‐based TCP (SD‐TCP) avoids these limitations; it uses only the textual difference information of the test cases themselves for prioritization. However, the time overhead on the sorting process of this method is not ideal, and the extreme test case inputs have an impact on the stability of the method. To address these problems, we propose a novel test case prioritization strategy, it first classifies the test cases more finely using the K‐medoids algorithm and then transforms the set into subsequences and improves the early diversity by greedy sorting within clusters. Finally, the test cases are selected through a polling strategy to compose the execution sequence. Extensive experimental results demonstrate that the proposed approach outperforms SD‐TCP in better time efficiency on test case prioritization; it also has a higher average percentage of fault detected (APFD) value than random prioritization (RP) and SD‐TCP.

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“…The Manhattan distance indicates the sum of the absolute axis distances of two points on a standard coordinate system. The formula is shown in Equation (2).…”

Section: Similarity Metricmentioning

confidence: 99%

“…However, frequent regression testing entails high testing costs. To reduce the overall testing time overhead, test suite reduction (TSR) 2 and test case prioritization (TCP) 3 are commonly used in regression testing. The TSR technique aims to discard the execution of some unimportant test cases after careful selection.…”

Section: Introductionmentioning

confidence: 99%

A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

Chen

Cai

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…Then, in regression testing scenarios, in order to save testing cost, the previous test cases are often clustered based on their execution profiles 74 or runtime information, 75 and then a subset of them is selected for the next round of testing. Among them, K‐Means is the most popular clustering technique used for test case reduction, 76 and other algorithms such as expectation maximization (EM) 77 and evolutionary clustering 78 are also adopted. The objects of clustering are usually passed test cases, failed test cases, or deviations 77 .…”

Section: Related Workmentioning

confidence: 99%

A lightweight adaptive random testing method for deep learning systems

Mao,

Song,

Chen

2023

Softw Pract Exp

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In recent years, deep learning (DL) systems are increasingly used in the safety‐critical fields such as autonomous driving, medical diagnosis, and financial service. Although these systems have demonstrated an outstanding performance in enhancing the accuracy of decision‐making, they pose significant challenges to the trustworthiness due to their limited interpretability and inherent uncertainty. Adaptive random testing (ART) has been proved as an effective approach for ensuring the reliability of DL systems. However, existing ART methods for DL systems incur a heavy overhead in test case selection due to the computation of distances. To address this issue, we propose a lightweight adaptive random testing (Lw‐ARTDL) method for DL systems. In our improved algorithm, we employ the K‐Means technique to divide the entire test suite into several subsets. Then, for a candidate test case, we only calculate distances between it and the test cases within the category to which it belongs. This partition strategy ensures that the selected test cases are more representative while significantly reducing the computational cost. To validate the proposed algorithm, the comparison experiments between Lw‐ARTDL and the original ARTDL algorithm are conducted on two typical DL systems. The experimental results show that Lw‐ARTDL significantly reduces the overhead of failure detection, and exhibits stronger failure detection capability compared to ARTDL in most similarity metrics.

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“…Xia and Zhang [42] present a novel evolutionary multi-objective optimization approach for reducing the cluster test suite. Specifically, the authors use the K-means technique to aggregate related test cases into a cluster.…”

Section: A Brief Review Of Research Articles On Trr Adopt Meta-heuris...mentioning

confidence: 99%

Comprehensive Review on the State-of- the-arts and Solutions to the Test Redundancy Reduction Problem with Taxonomy

Mizanur Rahman,

Kamal Z. Zamli,

Md. Abdul Kader

et al. 2023

ARASET

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The process of software testing is of utmost importance and requires a major allocation of resources. It has a substantial influence on the quality and dependability of software products. Nevertheless, as the quantity of test cases escalates, the feasibility of executing all of them diminishes, and the accompanying expenses related to preparation, execution time, and upkeep grow excessively exorbitant. The objective of Test Redundancy Reduction (TRR) is to mitigate this issue by determining a minimal subset of the test suite that satisfies all the requirements of the primary test suite while lowering the number of test cases. In order to attain this objective, multiple methodologies have been suggested, encompassing heuristics, meta-heuristics, exact algorithms, hybrid approaches, and machine-learning techniques. This work provides a thorough examination of prior research on TRR, addressing deficiencies and making a valuable contribution to the current scholarly understanding. The literature study encompasses a comprehensive examination of the complete chronology of TRR, incorporating all pertinent scholarly articles and practitioner-authored research papers published in English. This study aims to provide managers with valuable insights into the strengths and shortcomings of different TRR methodologies, enabling them to make well-informed decisions regarding the most appropriate approach for their specific needs. The primary objective of this study is to offer a comprehensive analysis of Test Result Reduction (TRR) and its consequential impact on mitigating expenses related to software testing. This study makes a valuable contribution to extant literature by elucidating the present state-of-the-art and delineating potential avenues for future research.

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Test Suite Reduction via Evolutionary Clustering

Cited by 11 publications

References 38 publications

A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

A lightweight adaptive random testing method for deep learning systems

Comprehensive Review on the State-of- the-arts and Solutions to the Test Redundancy Reduction Problem with Taxonomy

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