Startseite // SnT // News & E... // PhD Defense: Leveraging Execution Logs to Support Model Inference and Software Testing

PhD Defense: Leveraging Execution Logs to Support Model Inference and Software Testing

twitter linkedin facebook email this page
Add to calendar
Sprecher: Salma Messaoudi (SVV Research group)
Veranstaltung: Dienstag, den 29. Juni 2021 15:00 - 18:00

Please click on this link and join the online PhD defense (please note that the public part of the defense starts at 17:00).

Members of the defense committee:

  • Prof. Dr Fabrizio Pastore, University of Luxembourg, Chairman
  • Prof. Dr Domenico Bianculli, University of Luxembourg, Deputy Chairman
  • Prof. Dr Lionel Briand, University of Luxembourg, Supervisor
  • Prof. Leonardo Mariani, Università degli Studi di Milano-Bicocca, Member
  • Prof. Annibale Panichella, TU Delft University of Technology, Member



Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to correctly parse the entries in a log file to retrieve the actual events recorded in the log.
In the case of cyber-physical systems, execution logs are highly important because such systems integrate multiple third-party components where their source code is not always available. This limits the visibility of the system behavior to what is collected in the execution logs.
The increasing amount of logs produced by cyber-physical systems calls for 1) more advanced techniques for accurate log parsing, 2) scalable model inference that will enable efficient program comprehension and, 3) cost-effective software testing to ensure the quality of complex software systems.
The goal of this thesis is to investigate the usage of system execution logs to support different software engineering tasks in the context of cyber-physical systems where large amount of logs are being generated and require processing. Precisely, we propose a set of approaches to automate system behavior modeling from components logs and support regression testing, starting from an accurate and efficient log parsing.