Ant colony optimization for object-oriented unit test generation

Bruce, Dan, Menéndez, Héctor D. ORCID: https://orcid.org/0000-0002-6314-3725, Barr, Earl T. and Clark, David (2020) Ant colony optimization for object-oriented unit test generation. Dorigo, Marco, Stützle, Thomas, Blesa Aguilera, Maria J., Blum, Christian, Haman, Heiko, Heinrich, Maria Katherine and Strobel, Volker, eds. Swarm Intelligence: 12th International Conference, ANTS 2020, Barcelona, Spain, October 26–28, 2020, Proceedings. In: ANTS 2020, 26-28 Oct 2020, Barcelona, Spain. ISBN 9783030603755, e-ISBN 9783030603762. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-030-60376-2_3)

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Abstract

Generating useful unit tests for object-oriented programs is difficult for traditional optimization methods. One not only needs to identify values to be used as inputs, but also synthesize a program which creates the required state in the program under test. Many existing Automated Test Generation (ATG) approaches combine search with performance-enhancing heuristics. We present Tiered Ant Colony Optimization (Taco) for generating unit tests for object-oriented programs. The algorithm is formed of three Tiers of ACO, each of which tackles a distinct task: goal prioritization, test program synthesis, and data generation for the synthesised program. Test program synthesis allows the creation of complex objects, and exploration of program state, which is the breakthrough that has allowed the successful application of ACO to object-oriented test generation. Taco brings the mature search ecosystem of ACO to bear on ATG for complex object-oriented programs, providing a viable alternative to current approaches. To demonstrate the effectiveness of Taco, we have developed a proof-of-concept tool which successfully generated tests for an average of 54% of the methods in 170 Java classes, a result competitive with industry standard Randoop.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Part of the Lecture Notes in Computer Science book series (LNCS, volume 12421).
Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 12421)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 32816
Notes on copyright: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-60376-2_3.
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Depositing User: Hector Menendez Benito
Date Deposited: 08 Apr 2021 14:47
Last Modified: 09 Jun 2021 17:18
URI: https://eprints.mdx.ac.uk/id/eprint/32816

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