Enriching Mutation Testing With Innovative Method Invocation Mutation: Filling the Crucial Missing Piece of the Puzzle

Mutation testing aims to simulate real-world defects, but existing tools often struggle to replicate method invocation defects accurately. To address this, we propose MIN (Method INvocation mutator), which uses a mapping strategy to pair method names with corresponding values, ensuring that methods share argument and return types. This method enhances the feasibility and realism of mutants by considering factors such as library methods, access control, inheritance, and static methods. Experimental results show that integrating MIN into Major (a popular mutation tool) improves semantic similarity to real defects by 11%, increases mutant set diversity to 97.5%, and reduces undetected faults by 38.5%. Furthermore, MIN’s performance rivals that of state-of-the-art machine learning-based mutators like CodeBERT, with a 10x speed advantage over CodeBERT and 4x over DeepMutation in generating compilable mutants. These findings demonstrate that MIN can significantly enhance defect simulation and improve the efficiency of mutation testing.