Teaching learning based optimization with Pareto tournament for the multiobjective software requirements selection. (August 2015)

• Record Type: Journal Article
• Authors: Chaves-González, José M.; Pérez-Toledano, Miguel A.; Navasa, Amparo
• Is Part Of: Engineering applications of artificial intelligence. Volume 43(2015:Jul.)
• Page Start: 89
• Page End: 101
• Publication Date: 2015-08
• Subjects: Software requirements selection -- Multi-objective evolutionary algorithm -- Teaching learning based optimization -- Search Based Software Engineering -- Next Release Problem -- Swarm intelligence; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.04.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Software requirements selection is a problem which consists of choosing the set of new requirements which will be included in the next release of a software package. This NP-hard problem is an important issue involving several contradictory objectives which have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to satisfy the highest number of customer requirements. Furthermore, when managing real instances of the problem, the requirements tackled suffer interactions and other restrictions which make the problem even harder. In this paper, a novel multi-objective teaching learning based optimization (TLBO) algorithm has been successfully applied to several instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer׳s satisfaction. In addition, three interaction constraints have been also managed. In this context, the original TLBO algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out in order to prove the effectiveness of the multiobjective proposal. In fact, the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature.