Hierarchical Abstraction of Execution Traces for Program Comprehension

Understanding the dynamic behavior of a software system is one of the most important and time-consuming tasks for today’s software maintainers. In practice, understanding the inner workings of software requires studying the source code and documentation and inserting logging code to map high-level descriptions of the program behavior with low-level implementation, i.e., the source code. Unfortunately, for large codebases and large log files, such cognitive mapping can be quite challenging. To bridge the cognitive gap between the source code and detailed models of program behavior, prior software-execution mining research primarily focused on reducing the size of the low-level instruction execution traces. In contrast, in this thesis we propose a generic approach to present a semantic abstraction with different levels of functional granularity from full execution traces. Our approach mines multiple execution traces to identify frequent behaviors at multiple levels of abstraction, and then analyzes and labels individual execution traces according to the identified major functional behaviors of the system. To validate our technique, we conducted a case study on a large-scale subject program, Javac, to demonstrate the effectiveness of the mining result. Furthermore, the results of a user study demonstrate that our technique is capable of presenting users with a high-level comprehensible abstraction of execution behavior