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Almost every software system provides configuration options to tailor thesystem to the target platform and application scenario. Often, thisconfigurability renders the analysis of every individual system configurationinfeasible. To address this problem, researchers have proposed a diverse set ofsampling algorithms. We present a comparative study of 10 state-of-the-artsampling algorithms regarding their fault-detection capability and size ofsample sets. The former is important to improve software quality and the latterto reduce the time of analysis. In a nutshell, we found that samplingalgorithms with larger sample sets are able to detect higher numbers of faults,but simple algorithms with small sample sets, such as most-enabled-disabled,are the most efficient in most contexts. Furthermore, we observed that thelimiting assumptions made in previous work influence the number of detectedfaults, the size of sample sets, and the ranking of algorithms. Finally, wehave identified a number of technical challenges when trying to avoid thelimiting assumptions, which questions the practicality of certain samplingalgorithms.

A Comparison of 10 Sampling Algorithms for Configurable Systems