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Learning from evolved next release problem instances

Zhilei Ren 1 He Jiang 1 Jifeng Xuan 2 Shuwei Zhang 1 Zhongxuan Luo 1 
2 SPIRALS - Self-adaptation for distributed services and large software systems
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : Taking the Next Release Problem (NRP) as a case study, we intend to analyze the relationship between heuristics and the software engineering problem instances. We adopt an evolutionary algorithm to evolve NRP instances that are either hard or easy for the target heuristic (GRASP in this study), to investigate where a heuristic works well and where it does not, when facing a software engineering problem. Thereafter, we use a feature-based approach to predict the hardness of the evolved instances, with respect to the target heuristic. Experimental results reveal that, the proposed algorithm is able to evolve NRP instances with different hardness. Furthermore, the problem-specific features enables the prediction of the target heuristic's performance.
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Submitted on : Wednesday, November 26, 2014 - 10:31:45 AM
Last modification on : Thursday, January 20, 2022 - 4:12:24 PM
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Zhilei Ren, He Jiang, Jifeng Xuan, Shuwei Zhang, Zhongxuan Luo. Learning from evolved next release problem instances. GECCO - Genetic and Evolutionary Computation Conference, 2014, ACM SIGEVO, Jul 2014, Vancouver, BC, Canada. pp.189 - 190, ⟨10.1145/2598394.2598427⟩. ⟨hal-01087436⟩



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