Proceedings ArticleDOI
A Multi-objective Particle Swarm Optimization for Test Case Selection Based on Functional Requirements Coverage and Execution Effort
Luciano S. de Souza,Péricles B. C. de Miranda,Ricardo B. C. Prudêncio,Flávia A. Barros +3 more
- pp 245-252
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TLDR
A mechanism for functional TC selection which considers two objectives simultaneously: maximize requirements' coverage while minimizing cost in terms of TC execution effort and two multi-objective versions of PSO are implemented.Abstract:
Although software testing is a central task in the software lifecycle, it is sometimes neglected due to its high costs. Tools to automate the testing process minor its costs, however they generate large test suites with redundant Test Cases (TC). Automatic TC Selection aims to reduce a test suite 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). The majority of search-based works focus on single-objective selection. In this light, we developed a mechanism for functional TC selection which considers two objectives simultaneously: maximize requirements' coverage while minimizing cost in terms of TC execution effort. This mechanism was implemented as a multi-objective optimization process based on Particle Swarm Optimization (PSO). We implemented two multi-objective versions of PSO (BMOPSO and BMOPSO-CDR). The experiments were performed on two real test suites, revealing very satisfactory results (attesting the feasibility of the proposed approach). We highlight that execution effort is an important aspect in the testing process, and it has not been used in a multi-objective way together with requirements coverage for functional TC selection.read more
Citations
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Proceedings ArticleDOI
Reinforcement learning for automatic test case prioritization and selection in continuous integration
TL;DR: In this article, the Retecs 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.
Journal ArticleDOI
Effective Regression Test Case Selection: A Systematic Literature Review
TL;DR: This systematic literature review presents state-of-the-art research in effective regression test case selection techniques and observed that 70% of the studies being analyzed used cost as the effectiveness measure compared to 31% that use fault-detection capability and 16% that used coverage.
Proceedings ArticleDOI
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
TL;DR: This paper introduces Retecs, 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.
Journal ArticleDOI
A Uniform Representation of Hybrid Criteria for Regression Testing
TL;DR: The findings suggest that hybrid criteria of others can be described using the Merge and Rank formulations, and that the hybrid criteria the authors developed most often outperformed their constituent individual criteria.
Journal ArticleDOI
Search based constrained test case selection using execution effort
TL;DR: This work formulated the TC selection problem as a constrained search based optimization task, using requirements coverage as the fitness function to be maximized, and the execution effort of the selected TCs as a constraint in the search process.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Journal ArticleDOI
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TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
Proceedings ArticleDOI
A discrete binary version of the particle swarm algorithm
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
Journal ArticleDOI
Handling multiple objectives with particle swarm optimization
TL;DR: An approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions and indicates that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.