Print Email Facebook Twitter Hierarchical Abstraction of Execution Traces for Program Comprehension Title Hierarchical Abstraction of Execution Traces for Program Comprehension Author Dreef, Kaj (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology) Contributor van Deursen, Arie (mentor) Jones, James (mentor) Degree granting institution Delft University of Technology Date 2017-06-09 Abstract 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. Subject Dynamic AnalysisVisualizationHierarchical AbstractionLabelingsoftware To reference this document use: http://resolver.tudelft.nl/uuid:f30ded3b-7f35-4a93-af55-e1da122235f4 Part of collection Student theses Document type master thesis Rights © 2017 Kaj Dreef Files PDF dreef_msc_thesis.pdf 1.92 MB Close viewer /islandora/object/uuid:f30ded3b-7f35-4a93-af55-e1da122235f4/datastream/OBJ/view