Bypassing the Combinatorial Explosion: Using Similarity to Generate and Prioritize T-wise Test Suites for Large Software Product Lines

TL;DR

This paper introduces a scalable, similarity-based method for generating and prioritizing t-wise test suites in large Software Product Lines, overcoming limitations of existing approaches in handling large feature models and providing better test coverage.

Contribution

It presents a novel search-based approach and prioritization algorithms that enable effective testing of large SPLs, including the Linux kernel, addressing scalability and control issues.

Findings

Successfully tested on 124 feature models including Linux kernel

Demonstrated scalability and practicality of the approach

Improved test suite generation for large SPLs

Abstract

Software Product Lines (SPLs) are families of products whose commonalities and variability can be captured by Feature Models (FMs). T-wise testing aims at finding errors triggered by all interactions amongst t features, thus reducing drastically the number of products to test. T-wise testing approaches for SPLs are limited to small values of t -- which miss faulty interactions -- or limited by the size of the FM. Furthermore, they neither prioritize the products to test nor provide means to finely control the generation process. This paper offers (a) a search-based approach capable of generating products for large SPLs, forming a scalable and flexible alternative to current techniques and (b) prioritization algorithms for any set of products. Experiments conducted on 124 FMs (including large FMs such as the Linux kernel) demonstrate the feasibility and the practicality of our approach.