Abstract

In software industry, a common problem that the companies face is to decide what requirements should be implemented in the next release of the software. This paper aims to address the multi-objective next release problem using search based methods such as multi-objective evolutionary algorithms for empirical studies. In order to achieve the above goal, a requirement-dependency-based multi-objective next release model (MONRP/RD) is formulated firstly. The two objectives we are interested in are customers' satisfaction and requirement cost. A popular multi-objective evolutionary approach (MOEA), NSGA-II, is applied to provide the feasible solutions that balance between the two objectives aimed. The scalability of the formulated MONRP/RD and the influence of the requirement dependencies are investigated through simulations as well. This paper proposes an improved version of the multi-objective invasive weed optimization and compares it with various state-of-the-art multi-objective approaches on both synthetic and real-world data sets to find the most suitable algorithm for the problem.

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Cite this article

@article{cai-111217,
  author  = {Xinye Cai and Ou Wei and Zhiqiu Huang},
  title   = {Evolutionary Approaches for Multi-Objective Next Release Problem},
  journal = {Computing and Informatics},
  volume  = {31},
  number  = {4},
  pages   = {847--875},
  year    = {2012},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2012/4/evolutionary-approaches-for-multi-objective-next-release-problem/}
}
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