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R. M. Hierons, M. Li, X. Liu, J. A. Parejo, S. Segura, X. Yao. Many-objective test suite generation for software product lines. ACM Transactions on Software Engineering and Methodology, 29(1), 2020. [PDF] [C code]
R. M. Hierons, M. Li, X. Liu, J. A. Parejo, S. Segura, X. Yao. Many-objective test suite generation for software product lines. ACM Transactions on Software Engineering and Methodology, 29(1), 2020. [PDF] [C code]
What do we know about the problem
What do we know about the problem
An optimisation problem with nine objectives (i.e., the objective pairwise coverage, six prioritisation objectives, and two cost objectives: suite size and cost);
The objective coverage is more important;
The prioritisation objectives and cost objectives are not directly conflicting;
There is some tool which can generate high-quality solutions.
Idea:
Idea:
Treat the nine objectives differently - pairwise coverage first and then the others;
Try to improve prioritisation or cost on solutions with low cost or good prioritisation, respectively since they are not directly conflicting;
Use a high-quality solution produced by a tool as a seed, and then customise the search (use evolution strategy rather than genetic algorithm).
Result:
Result:
Experiments show that our algorithm GrES always returned a population in which all solutions provided full pairwise coverage, and achieved higher hypervolume values in most cases (what a higher hypervolume means can be seen from the range of the objective values obtained in the figure below).
Objective value range [min,max] of the solutions obtained by GrES and five multi-objective optimisation algorithms in a random feature model with 100 features.
Objective value range [min,max] of the solutions obtained by GrES and five multi-objective optimisation algorithms in a random feature model with 100 features.
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