@inproceedings {Simula.simula.714,
	title = {Industrial Experiences With Automated Regression Testing of a Legacy Database Application},
	journal = {IEEE Int. Conference on Software Maintenance},
	year = {2011},
	pages = {362-371},
	publisher = {IEEE Computer Society},
	type = {Conference},
	abstract = {This paper presents a practical approach and tool (DART) for functional black-box regression testing of complex legacy database applications. Such applications are important to many organizations, but are often difficult to change and consequently prone to regression faults during maintenance. They also tend to be built without particular considerations for testability and can be hard to control and observe. We have therefore devised a practical solution for functional regression testing that captures the changes in database state (due to data manipulations) during the execution of a system under test. The differences in changed database states between consecutive executions of the system under test, on different system versions, can help identify potential regression faults. In order to make the regression test approach scalable for large, complex database applications, classification tree models are used to prioritize test cases. The test case prioritization can be applied to reduce test execution costs and analysis effort. We report on how DART was applied and evaluated on business critical batch jobs in a legacy database application in an industrial setting, namely the Norwegian Tax Accounting System (SOFIE) at the Norwegian Tax Department (NTD). DART has shown promising fault detection capabilities and cost-effectiveness and has contributed to identify many critical regression faults for the past eight releases of SOFIE.},
	isbn = {978-1-4577-0663-9},
	author = {Rogstad, Erik and Arisholm, Erik and Briand, Lionel and Dalberg, Ronny and Rynning, Marianne},
	editor = {James R. Cordy A {Marcus} and Tonella, Paolo}
}
@article {airco06,
	title = {Applying and Combining Three Different Aspect Mining Techniques},
	journal = {Software Quality Journal},
	volume = {14},
	number = {3},
	year = {2006},
	month = {sep},
	pages = {209-231},
	abstract = {Understanding a software system at source-code level requires understanding the different concerns that it addresses, which in turn requires a way to identify these concerns in the source code. Whereas some concerns are explicitly represented by program entities (like classes, methods and variables) and thus are easy to identify, crosscutting concerns are not captured by a single program entity but are scattered over many program entities and are tangled with the other concerns. Because of their crosscutting nature, such crosscutting concerns are difficult to identify, and reduce the understandability of the system as a whole. In this paper, we report on a combined experiment in which we try to identify crosscutting concerns in the JHotDraw framework automatically. We first apply three independently developed aspect mining techniques to JHotDraw and evaluate and compare their results. Based on this analysis, we present three interesting combinations of these three techniques, and show how these combinations provide a more complete coverage of the detected concerns as compared to the original techniques individually. Our results are a first step towards improving the understandability of a system that contains crosscutting concerns, and can be used as a basis for refactoring the identified crosscutting concerns into aspects.},
	author = {Ceccato, Mariano and Marin, Marius and Mens, Kim and Moonen, Leon and Tonella, Paolo and Tourw{\'e}, Tom}
}
@inproceedings {Simula.SE.491,
	title = {A Qualitative Comparison of Three Aspect Mining Techniques},
	journal = {Proceedings 13th IEEE International Conference on Program Comprehension (ICPC)},
	year = {2005},
	publisher = {IEEE},
	type = {Conference},
	author = {Ceccato, Mariano and Marin, Marius and Mens, Kim and Moonen, Leon and Tonella, Paolo and Tourw{\'e}, Tom}
}
