Microservices are small and independently deployable services. They can be developed on different platforms and communicate via lightweight protocols, what makes them highly interoperable. The interoperability between microservices, as well as their reuse and customization needs make this kind of systems adequate to constitute a Software Product Line. However, there is no automatic approach to support the designing of Microservices-Based Product Lines (MBPLs). To move towards the development of MBPLs, this work presents an approach, named MOEA4MBPL, to extract Feature Models (FMs) from a set of microservices-based systems. These FMs intent to leverage interoperability, enabling the practitioners to reason about reuse and/or customization of functionalities. The proposed approach is based on multi-objective evolutionary algorithms, optimizing three objectives, namely precision and recall of products denoted by an FM, and conformance with existing dependencies between microservices. MOEA4MBPL was evaluated with six microservices-based systems, using the algorithms NSGA-II and SPEA2. Our approach was capable of finding FMs with good trade-off values of precision and recall, satisfying all dependencies among the microservices. SPEA2 found better fronts of solutions than NSGA-II, but the latter always executed faster and could find single solutions closer to an ideal solution than the former.

Software Product Lines Engineering (SPLE) is a software development approach intended for the development and maintenance of variable systems, i.e. systems that exist in many different variants. In the long run SPLE has many advantages. However, it requires a large upfront investment of time and money, which is why in practice Software Product Lines (SPLs) are rarely developed from scratch. Instead, they are often built using an extractive approach by which a set of existing system variants is consolidated (i.e. reverse engineered) into an SPL. A crucial part of this process is the construction of a variability model like a Feature Model (FM) that describes the common and variable parts of the system variants. In this paper we apply an approach for reverse engineering feature models based on a multi-objective optimization algorithm to the given challenge of constructing a feature model for a set of game variants and we present the results.