Conflict Detection for Edits on Extended Feature Models using Symbolic Graph Transformation

TL;DR

This paper introduces a symbolic graph transformation approach for conflict detection in extended feature models, effectively handling complex constraints with non-Boolean attributes to support concurrent model evolution.

Contribution

It presents a novel conflict detection method using symbolic graph transformation that manages constraints with first-order logic over extended feature models.

Findings

Effective conflict detection for extended feature models.

Integration of symbolic graphs with SMT solvers.

Applicable to models with complex attribute constraints.

Abstract

Feature models are used to specify variability of user-configurable systems as appearing, e.g., in software product lines. Software product lines are supposed to be long-living and, therefore, have to continuously evolve over time to meet ever-changing requirements. Evolution imposes changes to feature models in terms of edit operations. Ensuring consistency of concurrent edits requires appropriate conflict detection techniques. However, recent approaches fail to handle crucial subtleties of extended feature models, namely constraints mixing feature-tree patterns with first-order logic formulas over non-Boolean feature attributes with potentially infinite value domains. In this paper, we propose a novel conflict detection approach based on symbolic graph transformation to facilitate concurrent edits on extended feature models. We describe extended feature models formally with symbolic graphs and edit operations with symbolic graph transformation rules combining graph patterns with first-order logic formulas. The approach is implemented by combining eMoflon with an SMT solver, and evaluated with respect to applicability.