Evolution in dynamic software product lines

Abstract

Many software systems today provide support for adaptation and reconfiguration at runtime, in response to changes in their environment. Such adaptive systems are designed to run continuously and may not be shut down for reconfiguration or maintenance tasks. The variability of such systems has to be explicitly managed, together with mechanisms that control their runtime adaptation and reconfiguration. Dynamic software product lines (DSPLs) can help to achieve this. However, dealing with evolution is particularly challenging in a DSPL, as changes made at runtime can easily lead to inconsistencies. This paper describes the challenges of evolving DSPLs using an example cyber-physical system for home automation. We discuss the shortcomings of existing work and present a reference architecture to support DSPL evolution. To demonstrate its feasibility and flexibility, we implemented the proposed reference architecture for two different DSPLs: the aforementioned cyber-physical system, which uses feature models to describe its variability, and a runtime monitoring infrastructure, which is based on decision models. To assess the industrial applicability of our approach, we also implemented the reference architecture for a real-world DSPL, an automation software system for injection molding machines. Our results provide evidence on the flexibility, performance and industrial applicability of our approach.

Systematic Mapping Protocol: Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems

“…The aim of this work is to collect several proposals of architectures or design patterns to manage the reconfiguration process in DSPL for self-adaptive systems present in the literature. It will seek to analyze various proposals of constraints in the reconfiguration process, main errors during the system adaptation process or also some notion regarding how to maintain system stability and reliability during reconfigurations, ensuring the consistency of the software with respect to the models and the running system [3]. For this purpose, a systematic mapping of the literature for the most relevant articles of the last 11 years will be carried out, with the intention of collecting as much data as possible on the proposals to analyze the methodological approach used to reconfigure the solution domain of a software in an execution environment.…”

“…In [2], states that facing these challenges has led to the development of several approaches to adapt to changing needs, including self-adaptive, agent-based, autonomous, emergent, and bio-inspired systems. Dynamic software product lines (DSPLs) can meet these needs by providing a conceptual framework for managing variability in these types of systems, generating runtime changes, and managing system variability according to the needs of the environment [3]. This paper aims to present the definition of a protocol to systematically map the evolution of variability management in Dynamic Software Product Lines during the reconfiguration process of self-adaptive systems between the years 2010 and 2021.…”

Projectional Editing of Software Product Lines Using Multi-variant Model Editors

“…The platform comprises a set of reusable development artefacts that contain all commonalities and variabilities of the products being in the scope of the product line. In this regard, evolution of software product lines poses a significant field of research [17][18][19].…”

The interplay of compile-time and run-time options for performance prediction

“…Machine learning and configurable systems. Machine learning techniques have been widely considered in the literature to learn software configuration spaces and non-functional properties of software product lines [15,22,23,30,38,39,41,44,47,66,67]. Several works have proposed to predict performance of configurations, with several use-cases in mind for developers and users of configurable systems: the maintenance and interpretability of configuration spaces [54], the selection of an optimal configuration [15,39,41], the automated specialization of configurable systems [59], etc.…”