Evolution in dynamic software product lines

Abstract

In many domains systems need to run continuously and cannot be shut down for reconfiguration or maintenance tasks. Cyber-physical or cloud-based systems, for instance, thus often provide means to support their adaptation at runtime. The required flexibility and adaptability of systems suggests the application of Software Product Line (spl) principles to manage their variability and to support their reconfiguration. Specifically, Dynamic Software Product Lines (dspl) have been proposed to support the management and binding of variability at runtime. While spl evolution has been widely studied, it has so far not been investigated in detail in a dspl context. Variability models that are used in a dspl have to co-evolve and be kept consistent with the systems they represent to support reconfiguration even after changes to the systems at runtime. In this short paper we present a classification of the required operations for jointly evolving problem and solution space in a dspl. We analyze the impact of such operations on the consistency of a dspl and propose an approach to deal with the described issues. We describe a runtime monitoring system used in the domain of industrial automation software as an example of a dspl evolving at runtime to motivate and explain our work.

An Object Proxy-Based Dynamic Layer Replacement to Protect IoMT Applications

“…In [20], various problems of software evolution and their corresponding solutions are presented and classified into several types. Some required operations on evolution are formally described and analyzed to ensure the consistency before and after evolution.…”

Change Impact Analysis for Evolving Configuration Decisions in Product Line Use Case Models

“…They propose a classification of feature model changes that captures the impact of these changes on the feature model mappings and the mapped artifacts. Quinton et al (2015) propose yet another approach to ensure consistency of feature models and their mapped artifacts when feature models evolve. Dintzner et al (2014) compute the impact of a feature model change on the existing configurations of a product line by using partial dependency information in feature models.…”

Learning and evolution in dynamic software product lines

“…Context changes not anticipated during design-time are addressed by learning new adaptation rules dynamically, or by modifying and improving existing rules. On the other hand, evolution [4] has been proposed as a way to modify the DSPL configu-ration space [3,24,33]. The variability of the DSPL (in terms of features and their constraints) is changed based on insights gathered during system operation, while derived configurations are running.…”

“…Those run-time models are derived from a source model that can be evolved, and thus used to propagate changes to the run-time ones. In our previous work [3,24], we also studied the challenges related to the evolution of DSPLs. Compared to these contributions, we go further by using run-time feedback to decide how a DSPL is evolved, and take full advantage of both the knowledge produced by machine learning and the developers' expertise.…”