An Empirical Study of MBFL on Novice Programs Across Different Programming Languages

Programming education in computer science is growing rapidly, and debugging is a key challenge for novice programmers due to their limited experience. Mutation-Based Fault Localization (MBFL) is widely used in industry, but its effectiveness and challenges in novice programs need further study. While Python is a popular language in machine learning and data science, there is little research comparing fault localization in Python and Java for novice programmers. To bridge this gap, we conduct an empirical study to evaluate MBFL’s accuracy and execution overhead in common novice programming errors across different languages. We analyze how program features like code coverage and mutation score affect MBFL’s performance and whether these effects differ between languages. We also examine how MBFL’s effectiveness changes when suspiciousness scores are the same and how mutant noise and coincidental correct test cases vary across languages. Additionally, we propose a mutation confidence formula based on repair potential and behavioral difference to assess the usefulness of mutants in MBFL. Our study demonstrates that MBFL works well for novice fault localization in both Java and Python, with Python performing better. MBFL correctly identifies 45, 70, and 92 faults within the TOP-N (N = 1, 3, 5), proving its strong performance. However, tie problems, mutant noise, and coincidental correct test cases weaken MBFL, especially in Java. Results in both languages show a strong positive correlation between mutant confidence and fault localization accuracy, confirming the formula’s effectiveness across languages.