Ant Colony Optimization for Object-Oriented Unit Test Generation

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 performanceenhancing 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.

Software Testing or The Bugs’ Nightmare

“…EvoSuite employs algorithms mainly based on MOGAs. In the case of ACO, some tools like Dorylus [18] leverage this paradigm to create inputs for programs, even in the context of object-oriented programming where sequences of object's methods need to run in a specific order [19]. For Monte Carlo methods, tools like CoverMe or mathematical execution have proven to detect bugs even in the context of floating-point numbers [32].…”