Multi-objective ant colony optimization for requirements selection

Abstract

The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

Customer requirement acquisition system and requirement expression guidance based on ant colony optimization

“…A specificity of this mathematic model is that the solution space can be large. Thus, optimization algorithm, such as genetic algorithm [15][16][17] and ACO, 18,19 is widely employed to propose a set of optimal solutions to the customer. Various criteria are presented to extract optimal solution from the optimal solution set.…”

“…Du et al 23 developed a Stackelberg game theoretic model in order to achieve joint optimization of product family configuration and scaling design. Sagrado et al 19 employed ACO algorithm to analyze requirements.…”

Differential evolution with Pareto tournament for the multi-objective next release problem

“…As far as we know, the only two studies in which the MONRP is tackled by considering the interactions between the software requirements are the works by Sagrado et al [21,22], and by Souza et al [23]. All those works propose the use of different ACO strategies to solve the problem, but only in [22], the datasets used in the study are public, so our results will be compared with that study in Section 5.…”

“…All those works propose the use of different ACO strategies to solve the problem, but only in [22], the datasets used in the study are public, so our results will be compared with that study in Section 5. To this respect, it is worth mentioning that many of the related studies do not make public the information about the datasets used (probably for commercial reasons), so it is not possible to perform numerical comparisons with them.…”

“…In [24], the authors proposed a classification for the interactions depending on different software development projects. This classification can be grouped into four main categories [22]: (a) Implication or precedence: r i ) r j , which means that a requirement r i cannot be chosen if the requirement r j has not been previously chosen. (b) Combination or coupling: r i È r j , which means that a requirement r i has to be compulsorily included with the requirement r j .…”