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The Product Line Architecture (PLA) is a crucial artifact for the development of Software Product Lines. However, PLA is a complex artifact to be designed due to its large size and the multiple conflicting properties that need to be considered to ensure its quality, requiring a great effort for the architect. PLA designing has been formulated as an optimization problem aiming at improving some architectural properties in order to maximize both the feature modularization and the relational cohesion, and to minimize the class coupling. This kind of problem was successfully solved by multi-objective evolutionary algorithm. Nevertheless, most of existing approaches optimize PLA designs without applying the crossover operator, one of the fundamental genetic operators. To overcome these limitations, this paper aims to intensify the search-based PLA design optimization by presenting three crossover operators. These operators were empirically evaluated in quantitative and qualitative studies using three well-studied PLA designs. The experiments were conducted with eight experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results showed that there are significant differences among the use of only mutation and mutation with crossover. Also, we observed that the crossover operators contributed to generate solutions with better feature modularization. Finally, we could see that the proposed operators complement each other, since the experiment that combines at least two of the proposed operators achieved better results., Competing Interests: Conflict of InterestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Background: The software product lines (SPL) enable development teams to fully address a systematic reuse of shared assets to deliver a family of similar software products. Mobile applications are an obvious candidate for employing an SPL approach. This paper presents our research outcomes, based on empirical data from an industry-level development project. Two development teams were confronted with the same functionalities set to be delivered through a family of native mobile applications for Android and iOS., Methods: Empirical data was gathered before, during and after a year of full-time development. The data demonstrate the impact of a SPL approach by comparing the SPL and non-SPL multiple edition development. One family of products (Android apps) was developed using an SPL approach, while another (iOS apps), functionally the same, was developed without employing an SPL approach. The project generated a volume of raw and aggregated empirical data to support our research questions., Results: The paper reports a positive impact of an SPL approach on product quality (internal and external) and feature output per week. As data shows, it also increases the delivery of functionalities (240% in 6 more editions), while investing the same amount of effort needed for a single-edition development. As a result of system-supported separation of development and production code, developers had a high confidence in further development. On the other hand, the second team delivered less new functionalities, only two new application editions, and lower software quality than the team that manages multi-edition development by employing an SPL approach., Competing Interests: The authors declare that they have no competing interests., (© 2021 Pavlič et al.)

Context: Most specialized users (scientists) that use bioinformatics applications do not have suitable training on software development. Software Product Line (SPL) employs the concept of reuse considering that it is defined as a set of systems that are developed from a common set of base artifacts. In some contexts, such as in bioinformatics applications, it is advantageous to develop a collection of related software products, using SPL approach. If software products are similar enough, there is the possibility of predicting their commonalities, differences and then reuse these common features to support the development of new applications in the bioinformatics area., Objectives: This paper presents the PL-Science approach which considers the context of SPL and ontology in order to assist scientists to define a scientific experiment, and to specify a workflow that encompasses bioinformatics applications of a given experiment. This paper also focuses on the use of ontologies to enable the use of Software Product Line in biological domains., Method: In the context of this paper, Scientific Software Product Line (SSPL) differs from the Software Product Line due to the fact that SSPL uses an abstract scientific workflow model. This workflow is defined according to a scientific domain and using this abstract workflow model the products (scientific applications/algorithms) are instantiated., Results: Through the use of ontology as a knowledge representation model, we can provide domain restrictions as well as add semantic aspects in order to facilitate the selection and organization of bioinformatics workflows in a Scientific Software Product Line. The use of ontologies enables not only the expression of formal restrictions but also the inferences on these restrictions, considering that a scientific domain needs a formal specification., Conclusions: This paper presents the development of the PL-Science approach, encompassing a methodology and an infrastructure, and also presents an approach evaluation. This evaluation presents case studies in bioinformatics, which were conducted in two renowned research institutions in Brazil., (Copyright © 2015 Elsevier Inc. All rights reserved.)