Anomaly detection in Context-aware Feature Models

TL;DR

This paper formalizes anomaly detection in context-aware feature models and demonstrates how QBF solvers can efficiently identify anomalies, outperforming traditional SAT-based methods.

Contribution

It introduces a formal approach for anomaly detection in context-aware feature models using QBF solvers, improving efficiency over existing SAT-based techniques.

Findings

QBF solvers outperform traditional SAT-based anomaly detection methods

Formalization of anomaly analysis in context-aware feature models

Extension of HyVarRec reconfigurator with QBF-based analysis

Abstract

Feature Models are a mechanism to organize the configuration space and facilitate the construction of software variants by describing configuration options using features, i.e., a name representing a functionality. The development of Feature Models is an error prone activity and detecting their anomalies is a challenging and important task needed to promote their usage. Recently, Feature Models have been extended with context to capture the correlation of configuration options with contextual influences and user customizations. Unfortunately, this extension makes the task of detecting anomalies harder. In this paper, we formalize the anomaly analysis in Context-aware Feature Models and we show how Quantified Boolean Formula (QBF) solvers can be used to detect anomalies without relying on iterative calls to a SAT solver. By extending the reconfigurator engine HyVarRec, we present findings evidencing that QBF solvers can outperform the common techniques for anomaly analysis.