Search based constrained test case selection using execution effort

Souza, Luciana Sandra Bastos de; Prudêncio, Ricardo B. C.; Barros, Flávia A.; Aranha, Eduardo

doi:10.1016/j.eswa.2013.02.018

Cited by 28 publications

(12 citation statements)

References 23 publications

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“…Another study investigates coverage-based regression testing [9], using four common prioritization techniques: a test selection technique, a test suite minimization technique and a hybrid approach that combines selection and minimization. Similar approaches have been proposed using search-based algorithms [7,42], including swarm optimization [8] and ant colony optimization [22]. Walcott et al use genetic algorithms for time-aware regression test suite prioritization for frequent code rebuilding [40].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Spieker

Gotlieb

Marijan

et al. 2017

Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

199

176

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Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces R , a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The R method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the R method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying R on data extracted from three industrial case studies, we show for the rst time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing. CCS CONCEPTS•Software and its engineering → Software veri cation and validation; Software testing and debugging;

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Section: Related Workmentioning

confidence: 99%

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Spieker

Gotlieb

Marijan

et al. 2017

Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

199

176

View full text Add to dashboard Cite

show abstract

“…Several researchers have employed PSO, such as Khatibsyarbini et al [14], who used string distances to arrange the test instances and validated it on the real-world TSL dataset. To choose test cases based on redundancy, binary constraint PSO and its hybrid variants with local search algorithms were developed [15]. PSO was implemented with local search to select test cases, having goals of increased branch coverage and lower costs [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The original PSO, on the other hand, had issues, such as getting trapped in local optima and premature convergence [25]. According to the findings, upgraded and hybrid versions of PSO outperformed PSO for complicated systems [15]. One such algorithm is Quantum-behaved PSO (QPSO).…”

Section: Literature Reviewmentioning

confidence: 99%

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

Bajaj

Abraham

Ratnoo

et al. 2022

Sensors

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The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.

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“…A very promising approach to deal with the TC selection problem relies on the use of search optimization techniques (see [2], [3], [4], [5], [6]), which are the focus of our research. Here, the aim is to search for a subset of TCs which optimizes a given objective function (i.e., the given selection criterion).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Binary Multi-objective Particle Swarm Optimization with Local Search for Test Case Selection

Souza

Prudêncio

Barros

2014

2014 Brazilian Conference on Intelligent Systems

Self Cite

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During the software testing process a variety of test suites can be generated in order to evaluate and assure the quality of the products. However, in some contexts the execution of all suites does not fit the available resources (time, people, etc). In such cases, the suites could be automatically reduced based on some selection criterion. Automatic Test Case (TC) selection could be used to reduce the suites based on some selection criterion. This process can be treated as an optimization problem, aiming to find a subset of TCs which optimizes one or more objective functions (i.e., selection criteria). In this light, we developed two new mechanisms for TC selection which consider two objectives simultaneously: maximize branch coverage while minimizing execution cost (time). These mechanisms were implemented using multi-objective techniques based on Particle Swarm Optimization (PSO). Additionally, we create hybrid multi-objective selection algorithms in order to improve the results. The experiments were performed on the space program from the SIR repository, attesting the feasibility of the proposed hybrid strategies.

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Search based constrained test case selection using execution effort

Cited by 28 publications

References 23 publications

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

A Hybrid Binary Multi-objective Particle Swarm Optimization with Local Search for Test Case Selection

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