MediumDarwin: LittleDarwin Grows with Performance and Research-oriented Extensions

Abstract

Software testing is essential to ensure the reliability and correctness of software systems. However, the effectiveness of testing is highly dependent on the quality of the test suites themselves. Mutation analysis, a powerful technique for evaluating the quality of tests, introduces small changes into the code and checks whether the tests detect them. Despite its strengths, mutation analysis faces challenges in scalability due to the high computational cost of compiling and running tests against mutants. This paper presents MediumDarwin, a substantially upgraded version of the original LittleDarwin, initially introduced as a research prototype. Our enhanced version retains the original foundational architecture but introduces significant new capabilities and performance optimisations that transform it into a robust platform for both industrial use and advanced research. The enhancements made to LittleDarwin include: (1) persistent storage of mutation results in a relational database to facilitate advanced analysis, (2) coverage-based test selection optimisation to minimise test executions, (3) implementation of mutant schemata to reduce compilation overhead, (4) enhanced mutation operators alongside safeguards against non-compilable mutants, and (5) dynamic subsumption graph computation for efficient mutant analysis. These innovations collectively improve the tool's scalability and practical utility in software quality assurance in both industrial and research contexts. A screencast demonstrating the use of MediumDarwin is available at https://www.youtube.com/watch?v=Zsd3pZt63AE .