A methodology for controlling the size of a test suite
ACM Transactions on Software Engineering and Methodology (TOSEM)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Simulated annealing and genetic algorithms for optimal regression testing
Journal of Software Maintenance: Research and Practice
Incorporating varying test costs and fault severities into test case prioritization
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Partical Swarm Optimization Applied To The Atomic Cluster Optimization Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Proceedings of the 2006 international workshop on Automation of software test
TimeAware test suite prioritization
Proceedings of the 2006 international symposium on Software testing and analysis
Pareto efficient multi-objective test case selection
Proceedings of the 2007 international symposium on Software testing and analysis
Applying particle swarm optimization to software testing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An Estimation Model for Test Execution Effort
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Analysis of test suite reduction with enhanced tie-breaking techniques
Information and Software Technology
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Using hybrid algorithm for Pareto efficient multi-objective test suite minimisation
Journal of Systems and Software
ICSP'08 Proceedings of the Software process, 2008 international conference on Making globally distributed software development a success story
Randomized constraint solvers: a comparative study
Innovations in Systems and Software Engineering
Testing techniques in software engineering
Testing techniques in software engineering
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Test-Suite reduction using genetic algorithm
APPT'05 Proceedings of the 6th international conference on Advanced Parallel Processing Technologies
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Regression testing minimization, selection and prioritization: a survey
Software Testing, Verification & Reliability
Hi-index | 12.05 |
Software testing is essential to guarantee high quality products. However, it is a very expensive activity, particularly when manually performed. One way to cut down costs is by reducing the input test suites, which are usually large in order to fully satisfy the test goals. Yet, since large test suites usually contain redundancies (i.e., two or more test cases (TC) covering the same requirement/piece of code), it is possible to reduce them in order to respect time/people constraints without severely compromising coverage. In this light, we formulated the TC selection problem as a constrained search based optimization task, using requirements coverage as the fitness function to be maximized (quality of the resultant suite), and the execution effort (time) of the selected TCs as a constraint in the search process. Our work is based on the Particle Swarm Optimization (PSO) algorithm, which is simple and efficient when compared to other widespread search techniques. Despite that, besides our previous works, we did not find any other proposals using PSO for TC selection, neither we found solutions treating this task as a constrained optimization problem. We implemented a Binary Constrained PSO (BCPSO) for functional TC selection, and two hybrid algorithms integrating BCPSO with local search mechanisms, in order to refine the solutions provided by BCPSO. These algorithms were evaluated using two different real-world test suites of functional TCs related to the mobile devices domain. In the performed experiments, the BCPSO obtained promising results for the optimization tasks considered. Also, the hybrid algorithms obtained statistically better results than the individual search techniques.