An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem

Authors

  • Thaina Mariani Federal University of Paraná
  • Giovani Guizzo University College London
  • Silvia Regina Vergilio Federal University of Paraná
  • Aurora Pozo Federal University of Paraná

DOI:

https://doi.org/10.5753/jserd.2021.816

Keywords:

Multi-Objective Evolutionary Optimization, Grammatical Evolution, Iterated Racing, Software Testing

Abstract

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied to solve hard real software engineering problems. However, to choose and design a MOEA is considered a difficult task, since there are several parameters and components to be configured. These aspects directly impact the generated solutions and the performance of MOEAs. In this sense, this paper proposes an approach for the automatic generation of MOEAs applied to the Integration and Test Order (ITO) problem. Such a problem refers to the generation of optimal sequences of units for integration testing. The approach includes a set of parameters and components of different MOEAs, and is implemented with two design algorithms: Grammatical Evolution (GE) and Iterated Racing (irace). Evaluation results are presented, comparing the MOEAs generated by both design algorithms. Furthermore, the generated MOEAs are compared to two well-known MOEAs used in the literature to solve the ITO problem. Results show that the MOEAs generated with GE and irace perform similarly, and both outperform traditional MOEAs. The approach can reduce efforts spent to design and configure MOEAs, and serves as basis for implementing solutions to other software engineering problems.

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Published

2021-04-05

How to Cite

Mariani, T., Guizzo, G., Vergilio, S. R., & Pozo, A. (2021). An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem. Journal of Software Engineering Research and Development, 9(1), 4:1 – 4:18. https://doi.org/10.5753/jserd.2021.816

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Research Article