Inferring Hierarchical Motifs from Execution Traces

Saba Alimadadi, Ali Mesbah, and Karthik Pattabiraman, To appear in the ACM/IEEE International Conference on Software Engineering (ICSE), 2018. (Acceptance Rate: 21%). [ PDF | Talk ]

Abstract: Program comprehension is a necessary step for performing many software engineering tasks. Dynamic analysis is effective in producing execution traces that assist comprehension. Traces are rich sources of information regarding the behaviour of a program. However, it is challenging to gain insight from traces due to their overwhelming amount of data and complexity. We propose a generic technique for facilitating comprehension by inferring recurring execution motifs. Inspired by bioinformatics, motifs are patterns in traces that are flexible to small changes in execution, and are captured in a hierarchical model. The hierarchical nature of the model provides an overview of the behaviour at a high-level, while preserving the execution details and intermediate levels in a structured manner. We design a visualization that allows developers to observe and interact with the model. We implement our approach in an open-source tool, called Sabalan, and evaluate it through a user experiment. The results show that using Sabalan improves developers’ accuracy in performing comprehension tasks by 54%.

This entry was posted in papers and tagged , , , . Bookmark the permalink.