Paper Info
Title
Learning from evolved next release problem instances
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.
Year
DOI
Venue
2014
10.1145/2598394.2598427
GECCO (Companion)
Keywords
Field
DocType
genetic algorithm,methodologies,next release problem,problem hardness
Heuristic,GRASP,Evolutionary algorithm,Computer science,Heuristics,Artificial intelligence,Machine learning,Genetic algorithm
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
5
Name
Order
Citations
PageRank
Zhilei Ren132024.57
He Jiang250349.89
Jifeng Xuan353928.76
Shuwei Zhang410.69
Zhongxuan Luo528051.48