A Systematic Literature Review on Regression Test Case Prioritization

Rahmani, Ani; Ahmad, Sabrina; Jalil, Intan Ermahani A.; Herawan, Adhitia Putra

doi:10.14569/ijacsa.2021.0120929

Cited by 5 publications

(9 citation statements)

References 59 publications

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“…G1: Increasing test suite's rate of fault detection 3,[6][7][8]17,[19][20][21]25,27,[29][30][31][32][33]35,35,36,42,[44][45][46][47][48][48][49][50]54,55,[62][63][64] Coverage-based information, 3,19,27,30,31,33,44,63 Requirements information, 6,21,33,62 Test execution/fault detection history 3,[6][7][8]20,27,31,33,…”

Section: Goal Information Need Metricmentioning

confidence: 99%

“…Rahmani et al 21 conducted a systematic review of regression TCP techniques proposed from 2017 to 2020. The authors classified the techniques based on TCP approaches (e.g., risk based and history based).…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, to evaluate the outcomes, these techniques use metrics including average percentage of fault detected (APFD) and its variants, coverage-based metrics, and metrics related to execution time. 3,20,21 Various regression test selection and prioritization techniques have been proposed in the literature. 7 However, the adoption rate of these techniques in industry is not encouraging, and only a few techniques have been evaluated in the industry context.…”

Section: Introductionmentioning

confidence: 99%

“…We looked at 11 systematic reviews on regression testing published during the last six years (i.e., 2017 to 2022) 3,6,20,21,[27][28][29][30][31][32][33] to learn the recent trends in regression testing techniques and see which goals, information needs, and measures are considered. Six of the 11 studies reviewed the techniques regardless of application domain or techniques type.…”

Section: Introductionmentioning

confidence: 99%

“…Regression testing techniques utilize multiple sources of information, including coverage information, requirement information, and test execution history. Furthermore, to evaluate the outcomes, these techniques use metrics including average percentage of fault detected (APFD) and its variants, coverage‐based metrics, and metrics related to execution time 3,20,21 …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Using goal–question–metric to compare research and practice perspectives on regression testing

Minhas

Koppula

Petersen

et al. 2022

J Software Evolu Process

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Regression testing is challenging because of its complexity and the amount of effort and time it requires, especially in large-scale environments with continuous integration and delivery. Regression test selection and prioritization techniques have been proposed in the literature to address the regression testing challenges, but adoption rates of these techniques in industry are not encouraging. One of the possible reasons could be the disparity in the regression testing goals in industry and literature. This work compares the research perspective to industry practice on regression testing goals, corresponding information needs, and metrics required to evaluate these goals. We have conducted a literature review of 44 research papers and a survey with 56 testing practitioners. The survey comprises 11 interviews and 45 responses to an online questionnaire. We identified that industry and research accentuate different regression testing goals. For instance, the literature emphasizes increasing the fault detection rates of test suites and early identification of critical faults. In contrast, the practitioners' focus is on test suite maintenance, controlled fault slippage, and awareness of changes. Similarly, the literature suggests maintaining information needs from test case execution histories to evaluate regression testing techniques based on various metrics, whereas, at large, the practitioners do not use the metrics suggested in the literature. To bridge the research and practice gap, based on the literature and survey findings, we have created a goal-question-metric (GQM) model that maps the regression testing goals, associated information needs, and metrics from both perspectives. The GQM model can guide researchers in proposing new techniques closer to industry contexts. Practitioners can benefit from information needs and metrics presented in the literature and can use GQM as a tool to follow their regression testing goals.

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Section: Goal Information Need Metricmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using goal–question–metric to compare research and practice perspectives on regression testing

Minhas

Koppula

Petersen

et al. 2022

J Software Evolu Process

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Machine Learning for Software Engineering: A Tertiary Study

2023

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Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6 117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.

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Directed Search Based on Improved Whale Optimization Algorithm for Test Case Prioritization

Yang¹,

Li²,

Xing³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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With the advent of the information age, the iterative speed of software update is gradually accelerating which makes software development severely limited by software testing. Test case prioritization is an effective way to accelerate software testing progress. With the introduction of heuristic algorithm to this task, the processing efficiency of test cases has been greatly improved. However, to overcome the shortcomings of slow convergence speed and easy fall into local optimum, the improved whale optimization algorithm is proposed for test case prioritization. Firstly, a model called n-dimensional directed search space is established for the swarm intelligence algorithm. Secondly, the enhanced whale optimization algorithm is applied to test case prioritization while the backtracking behavior is conducted for individuals when hitting the wall. In addition, a separate storage space for Pareto second optimization is also designed to filter the optimal solutions of the multi-objective tasks. Finally, both single-objective and multi-objective optimization experiments are carried out for open source projects and real-world projects, respectively. The results show that the improved whale optimization algorithm using n-dimensional directed search space is more conducive to the decisions of test case prioritization with fast convergence speed.

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A Systematic Literature Review on Regression Test Case Prioritization

Cited by 5 publications

References 59 publications

Using goal–question–metric to compare research and practice perspectives on regression testing

Using goal–question–metric to compare research and practice perspectives on regression testing

Machine Learning for Software Engineering: A Tertiary Study

Directed Search Based on Improved Whale Optimization Algorithm for Test Case Prioritization

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