Feature-Aware Verification

TL;DR

This paper presents feature-aware verification, a method that uses variability encoding and model checking to efficiently detect feature interactions in software product lines, demonstrated on an email system.

Contribution

It introduces a novel verification approach that supports feature-specific properties and improves detection efficiency without exponential complexity.

Findings

Feature interactions can be automatically detected using feature-local specifications.

Variability encoding significantly enhances verification performance.

The approach is validated on an AT&T-based email system.

Abstract

A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.