TY  - JOUR
AV  - public
VL  - 9
SP  - 1
Y1  - 2021/04/05/
EP  - 18
TI  - An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem
N1  - Copyright (c) 2021 Thaina Mariani, Giovani Guizzo, Silvia Regina Vergilio, Aurora Pozo. This work is licensed under a Creative Commons Attribution 4.0 International License
UR  - https://doi.org/10.5753/jserd.2021.816
N2  - 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.
ID  - discovery10131840
A1  - Guizzo, G
A1  - Mariani, T
A1  - Vergilio, SR
A1  - Pozo, ATR
JF  - Journal of Software Engineering Research and Development
ER  -