A Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samples

Date
2022-01-04
Authors
Heradio, Ruben
Fernandez-Amoros, David
Ruiz, Victoria
Cobo, Manuel J.
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Software systems tend to become more and more configurable to satisfy the demands of their increasingly varied customers. Exhaustively testing the correctness of highly configurable software is infeasible in most cases because the space of possible configurations is typically colossal. This paper proposes addressing this challenge by (i) working with a representative sample of the configurations, i.e., a ``uniform'' random sample, and (ii) processing the results of testing the sample with a rule induction system that extracts the faults that cause the tests to fail. The paper (i) gives a concrete implementation of the approach, (ii) compares the performance of the rule learning algorithms AQ, CN2, LEM2, PART, and RIPPER, and (iii) provides empirical evidence supporting our procedure.
Description
Keywords
Soft Computing: Theory Innovations and Problem Solving Benefits, highly configurable software, rule induction, software product line, software testing, uniform random sampling
Citation
Rights
Access Rights
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.