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.
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Notes
- 1.
For our implementation of Taco the following values were used: \(\rho _0 = 50\), \(\gamma _m = 0.5\), \(\delta _m = 0.05\). With a minimum pheromone of 1 and maximum of 100.
- 2.
JaCoCo is a free code coverage library for Java: https://www.eclemma.org/jacoco/.
- 3.
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Bruce, D., Menéndez, H.D., Barr, E.T., Clark, D. (2020). Ant Colony Optimization for Object-Oriented Unit Test Generation. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_3
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