Incremental covering array failure characterization in large configuration spaces

  • Authors:

  • Sandro Fouché;Myra B. Cohen;Adam Porter

  • Affiliations:

  • University of Maryland, College Park, MD, USA;University of Nebraska-Lincoln, Lincoln, NE, USA;University of Maryland, College Park, MD, USA

  • Venue:
  • Proceedings of the eighteenth international symposium on Software testing and analysis
  • Year:
  • 2009

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Abstract

The increasing complexity of configurable software systems has created a need for more intelligent sampling mechanisms to detect and characterize failure-inducing dependencies between configurations. Prior work - in idealized environments - has shown that test schedules based on a mathematical object, called a covering array, in combination with classification techniques, can meet this need. Applying this approach in practice, however, is tricky because testing time and resource availability are unpredictable, and because failure characteristics can change from release to release. With current approaches developers must set a key covering array parameter (its strength) based on estimated release times and failure characterizations. This will influence the outcome of their results. In this paper we propose a new approach that incrementally builds covering array schedules. This approach begins at a low strength, and then iteratively increases strength as resources allow. At each stage previously tested configurations are reused, thus avoiding duplication of work. With the incremental approach developers need never commit to a specific covering array strength. Instead, by using progressively stronger covering array schedules, failures due to few configuration dependencies can be found and classified as soon and as cheaply as possibly. Additionally, it eliminates the risks of committing to overly strong test schedules. We evaluate this new approach through a case study on three consecutive releases of MySQL, an open source database. Our results suggest that our approach is as good or better than previous approaches, costing less in most cases, and allowing greater flexibility in environments with unpredictable development constraints.