Abstract-To prevent ill-formed configurations, highly configurable software often allows defining constraints over the available options. As these constraints can be complex, fixing a configuration that violates one or more constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a violated constraint. We have evaluated our approach with three different strategies for handling constraint interactions, on data from five open source projects. Our evaluation shows that, even with the most complex strategy, our approach generates complete fix lists that are mostly short and concise, in a fraction of a second.
Delivering configurable solutions, that is products tailored to the requirements of a particular customer, is a priority of most B2B and B2C markets. These markets now heavily rely on interactive configurators that help customers build complete and correct products. Reliability is thus a critical requirement for configurators. Yet, our experience in industry reveals that many configurators are developed in an ad hoc manner, raising correctness and maintenance issues. In this paper, we present a vision to re-engineering more reliable configurators and the challenges it poses. The first challenge is to reverse engineer from an existing configurator the variability information, including complex rules, and to consolidate it in a variability model, namely a feature model. The second challenge is to forward engineer a new configurator that uses the feature model to generate a customized graphical user interface and the underlying reasoning engine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.