Heuristic and exact algorithms for product configuration in software product lines

Abstract

Software product line (SPL) is a set of software applications that share a common set of features satisfying the specific needs of a particular market segment. SPL engineering is a paradigm to develop software applications that commonly use a feature model to capture and document common and variable features, and their relationships. A big challenge is to derive one product among all possible products in the SPL, which satisfies the business and customer requirements. This task is known as product configuration. Although product configuration has been extensively investigated in the literature, customer's preferences are frequently neglected. In this paper, we propose a novel approach to configure a product that considers both qualitative and quantitative feature properties. We model the product configuration task as a combinatorial optimization problem, and heuristic and exact algorithms are proposed. As far as we are concerned, this proposal is the first work in the literature that considers feature properties in both leaf and nonleaf features. Computational experiments showed that the best of our heuristics found optimal solutions for all instances where those are known. For the instances where optimal solutions are not known, our heuristic outperformed the best solution obtained by a one‐hour run of the exact algorithm by up to 67.89%.

Exact algorithms for the product configuration problem

“…Pereira et al. (2017) proposed the PCP variant studied in this paper. They also study the so‐called cross‐tree constraints.…”

“…In this section, we describe the backtracking algorithm of Pereira et al. (2017). Backtracking is an enumeration algorithm that has been widely used in the literature (Morabito and Arenales, 1994; Noronha et al., 2010; Romero et al., 2013; Ribeiro and Riveaux, 2019; Wang et al., 2020), where infeasible and suboptimal solutions are eliminated without being explicitly examined (Cormen et al., 2009).…”

Many-objective Search-based Selection of Software Product Line Test Products with Nautilus

“…To determine the pairwise coverage, we use the AETG algorithm [1]. The cost of leaf and non-leaf features are considered, that is, we assume that the leaf and non-leaf features in a given FM have concrete implementations, as adopted by Pereira et al [14]. All objectives functions are normalized in the range [0, 1], where 0 is the best value, that is, they should be minimized.…”

Improving complex SMT strategies with learning

“…Grammatical evolution (Ryan et al, 1998) could also be considered in order to improve Z3 performance. In the context of search-based software engineering presented in Harman (2011), optimization techniques can be used for improving software and code generation and, recently, software configuration problems Pereira et al (2017). Finally, autonomous search (Hamadi et al, 2011) aims at providing tools and methods for designing solving techniques without any expert knowledge.…”