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Accelerating covering array generation by combinatorial join for industry scale software testing.

Hiroshi Ukai1,2, Xiao Qu3, Hironori Washizaki1

  • 1Waseda University, Tokyo, Japan.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new method to generate covering arrays for combinatorial interaction testing. This approach significantly reduces test case generation time by reusing existing arrays.

Keywords:
Automated combinatorial test generationAutomated testingCombinatorial explosionCombinatorial interaction testingCombinatorial testingConstrained covering arrayCovering arraySoftwareSoftware testingVariable strength covering array

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Area of Science:

  • Computer Science
  • Software Engineering
  • Combinatorics

Background:

  • Combinatorial interaction testing verifies systems with many input parameters using covering arrays.
  • Generating covering arrays with parameter constraints is computationally expensive and challenging.
  • Existing methods struggle with efficiency when handling complex constraints.

Purpose of the Study:

  • To introduce a novel mathematical operation for constructing covering arrays.
  • To reduce the computational cost and time required for generating covering arrays.
  • To improve the efficiency of combinatorial interaction testing.

Main Methods:

  • Proposed a new mathematical operation: "weaken-product based combinatorial join".
  • This operation constructs new covering arrays by combining two existing ones.
  • The method reuses existing covering arrays to enhance parallelism and save resources.

Main Results:

  • The "weaken-product based combinatorial join" significantly reduces covering array generation time.
  • Observed time reductions ranging from 13% to 96% across various use cases.
  • The method maintains the combinatorial coverage of the original arrays.

Conclusions:

  • The proposed weaken-product based combinatorial join offers an efficient solution for covering array generation.
  • This technique addresses the challenge of handling parameter constraints in combinatorial testing.
  • The method leads to substantial computational savings and improved testing efficiency.