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- PublicationPreliminary Study of Multi-objective Features Selection for Evolving Software Product LinesWhen dealing with software-intensive systems, it is often beneficial to consider families of similar systems together. A common task is then to identify the particular product that best fulfils a given set of desired product properties. Software Product Lines Engineering (SPLE) provides techniques to design, implement and evolve families of similar systems in a systematic fashion, with variability choices explicitly represented, e.g., as Feature Models. The problem of picking the 'best' product then becomes a question of optimising the Feature Configuration. When considering multiple properties at the same time, we have to deal with multi-objective optimisation, which is even more challenging. While change and evolution of software systems is the common case, to the best of our knowledge there has been no evaluation of the problem of multi-objective optimisation of evolving Software Product Lines. In this paper we present a benchmark of large scale evolving Feature Models and we study the behaviour of the state-of-the-art algorithm (SATIBEA). In particular, we show that we can improve both the execution time and the quality of SATIBEA by feeding it with the previous configurations: our solution converges nearly 10 times faster and gets an 113% improvement after one generation of genetic algorithm.
458Scopus© Citations 2
- PublicationMILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product LinesSoftware Product Lines Engineering (SPLE) proposes techniques to model, create and improve groups of related software systems in a systematic way, with different alternatives formally expressed, e.g., as Feature Models. Selecting the 'best' software system(s) turns into a problem of improving the quality of selected subsets of software features (components) from feature models, or as it is widely known, Feature Configuration. When there are different independent dimensions to assess how good a software product is, the problem becomes even more challenging- it is then a multi-objective optimisation problem. Another big issue for software systems is evolution where software components change. This is common in the industry but, as far as we know, there is no algorithm designed to the particular case of multi-objective optimisation of evolving software product lines. In this paper we present MILPIBEA, a novel hybrid algorithm which combines the scalability of a genetic algorithm (IBEA) with the accuracy of a mixed-integer linear programming solver (IBM ILOG CPLEX). We also study the behaviour of our solution (MILPIBEA) in contrast with SATIBEA, a state-of-the-art algorithm in static software product lines. We demonstrate that MILPIBEA outperforms SATIBEA on average, especially for the most challenging problem instances, and that MILPIBEA is the one that continues to improve the quality of the solutions when SATIBEA stagnates (in the evolving context).
340Scopus© Citations 4
- PublicationIs seeding a good strategy in multi-objective feature selection when feature models evolve?Context: When software architects or engineers are given a list of all the features and their interactions (i.e., a Feature Model or FM) together with stakeholders 'preferences' their task is to find a set of potential products to suggest the decision makers. Software Product Lines Engineering (SPLE) consists in optimising those large and highly constrained search spaces according to multiple objectives reflecting the preference of the different stakeholders. SPLE is known to be extremely skill- and labour-intensive and it has been a popular topic of research in the past years.Objective: This paper presents the first thorough description and evaluation of the related problem of evolving software product lines. While change and evolution of software systems is the common case in the industry, to the best of our knowledge this element has been overlooked in the literature. In particular, we evaluate whether seeding previous solutions to genetic algorithms (that work well on the general problem) would help them to find better/faster solutions.Method: We describe in this paper a benchmark of large scale evolving FMs, consisting of 5 popular FMs and their evolutions – synthetically generated following an experimental study of FM evolution. We then study the performance of a state-of-the-art algorithm for multi-objective FM selection (SATIBEA) when seeded with former solutions.Results: Our experiments show that we can improve both the execution time and the quality of SATIBEA by feeding it with previous configurations. In particular, SATIBEA with seeds proves to converge an order of magnitude faster than SATIBEA alone.Conclusion: We show in this paper that evolution of FMs is not a trivial task and that seeding previous solutions can be used as a first step in the optimisation - unless the difference between former and current FMs is high, where seeding has a limited impact.
Scopus© Citations 12 435