Improving Efficiency and Scalability of Formula-based Debugging
This work addresses scalability issues for software developers using debugging tools, though it is incremental as it builds on existing formula-based methods.
The paper tackled the computational expense and limited use of passing test information in formula-based debugging by proposing on-demand formula computation and clause weighting, resulting in improved efficiency and accuracy.
Formula-based debugging techniques are becoming increasingly popular, as they provide a principled way to identify potentially faulty statements together with information that can help fix such statements. Although effective, these approaches are computationally expensive, which limits their practical applicability. Moreover, they tend to focus on failing test cases alone, thus ignoring the wealth of information provided by passing tests. To mitigate these issues, we propose two techniques: on-demand formula computation (OFC) and clause weighting (CW). OFC improves the overall efficiency of formula-based debugging by exploring all and only the parts of a program that are relevant to a failure. CW improves the accuracy of formula-based debugging by leveraging statistical fault-localization information that accounts for passing tests. Our empirical results show that both techniques are effective and can improve the state of the art in formula-based debugging.