Hybrid Artificial Bee Colony Algorithm for Test Case Generation and Optimization

The generation of high-quality test cases remains challenging due to combinatorial explosion and difficulty balancing exploration-exploitation in complex parameter spaces. This paper presents a novel Hybrid Artificial Bee Colony (ABC) algorithm that uniquely combines ABC optimization with Simulated Annealing temperature control and adaptive scout mechanisms for automated test case generation. The approach employs a four-tier categorical fitness function discriminating between boundary-valid, valid, boundary-invalid, and invalid values, with first-occurrence bonuses ensuring systematic exploration. Through comprehensive empirical validation involving 970 test suite generations across 97 parameter configurations, the hybrid algorithm demonstrates 68.3% improvement in fitness scores over pairwise testing (975.9 ± 10.6 vs. 580.0 ± 0.0, p < 0.001, d = 42.61). Statistical analysis identified three critical parameters with large effect sizes: MutationRate (d = 106.61), FinalPopulationSelectionRatio (d = 42.61), and TotalGenerations (d = 19.81). The value discrimination system proved essential, uniform weight configurations degraded performance by 7.25% (p < 0.001), while all discriminating configurations achieved statistically equivalent results, validating the architectural design over specific weight calibration.