Leveraging Execution Logs to Support Model Inference and Software Testing

Messaoudi, Salma

Reference : Leveraging Execution Logs to Support Model Inference and Software Testing


	Document type :	Dissertations and theses : Doctoral thesis
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/48513

Title :	Leveraging Execution Logs to Support Model Inference and Software Testing
Language :	English
Author, co-author :	Messaoudi, Salma [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) > >]
Publication date :	29-Jun-2021
Institution :	University of Luxembourg, Luxembourg
Name of the degree :	Docteur en Informatique
Supervisor :	Briand, Lionel
	Shin, Donghwan
President of the jury :	Pastore, Fabrizio
Member of the jury :	Bianculli, Domenico
	Mariani, Leonardo
	Panichella, Annibale
Keywords :	[en] Software testing ; System logs ; Regression testing ; Program slicing ; Model inference ; Search-based software engineering
Abstract :	[en] 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 enabling efficient program comprehension and, 3) cost-effective software testing to ensuring the quality of complex soft- ware systems.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/48513

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