A study of the bi-objective next release problem

One important issue addressed by software companies is to determine which features should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while entailing the minimum cost for the company. This problem is known as the Next R...

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Bibliographic Details
Published in:Empirical software engineering : an international journal 2011-02, Vol.16 (1), p.29-60
Main Authors: Durillo, Juan J., Zhang, Yuanyuan, Alba, Enrique, Harman, Mark, Nebro, Antonio J.
Format: Article
Language:English
Subjects:
Benchmarking
Compilers
Computer programs
Computer Science
Customers
Interpreters
Mathematical models
Minimum cost
Optimization
Programming Languages
Software Engineering/Programming and Operating Systems
Software packages
Strategy
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Summary:One important issue addressed by software companies is to determine which features should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while entailing the minimum cost for the company. This problem is known as the Next Release Problem (NRP). Since minimizing the total cost of including new features into a software package and maximizing the total satisfaction of customers are contradictory objectives, the problem has a multi-objective nature. In this work, we apply three state-of-the-art multi-objective metaheuristics (two genetic algorithms, NSGA-II and MOCell, and one evolutionary strategy, PAES) for solving NRP. Our goal is twofold: on the one hand, we are interested in analyzing the results obtained by these metaheuristics over a benchmark composed of six academic problems plus a real world data set provided by Motorola; on the other hand, we want to provide insight about the solution to the problem. The obtained results show three different kinds of conclusions: NSGA-II is the technique computing the highest number of optimal solutions, MOCell provides the product manager with the widest range of different solutions, and PAES is the fastest technique (but with the least accurate results). Furthermore, we have observed that the best solutions found so far are composed of a high percentage of low-cost requirements and of those requirements that produce the largest satisfaction on the customers as well.
ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-010-9147-3