Numerous program slicing approaches aim to help developers troubleshoot regression failures - one of the most time-consuming development tasks. The main idea behind these approaches is to identify a subset of interdependent program statements relevant to the failure, minimizing the amount of code developers need to inspect. Accuracy and reduction rate achieved by slicing are the key considerations toward their applicability in practice: inspecting only the statements in a slice should be faster and more efficient than inspecting the code in full. In this paper, we report on our experiment applying one of the most recent and accurate slicing approaches, dual slicing, to the task of troubleshooting regression failures. As subjects, we use projects from the popular Defects4J benchmark and a systematically-collected set of eight large, open-source client-library project pairs with at least one library upgrade failure, which we refer to as LibRench. The results of our experiments show that the produced slices, while effective in reducing the scope of manual inspection, are still very large to be comfortably analyzed by a human. When inspecting these slices, we observe that most statements in a slice deal with the propagation of information between changed code blocks; these statements are essential for obtaining the necessary context for the changes but are not responsible for the failure directly. Motivated by this insight, we propose a novel approach, implemented in a tool named INPRESS, for further reducing the size of a slice by accurately identifying and summarizing the propagation-related code blocks. Our evaluation of INPRESS shows that it is able to produce slices that are 76% shorter than the original ones (207 vs. 2,007 execution statements, on average), thus, reducing the amount of information developers need to inspect without losing the necessary contextual information.

@inproceedings{BadihiALR23,
  title = {Responsibility in Context: On Applicability of Slicing in Semantic Regression Analysis},
  author = {Sahar Badihi and Khaled Ahmed and Yi Li and Julia Rubin},
  booktitle = {ICSE},
  year = {2023},
  doi = {10.1109/icse48619.2023.00057}
}