Using test case metrics to predict code quality and effort
ACM SIGSOFT Software Engineering Notes
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Using evolutionary algorithms for the unit testing of object-oriented software
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A software testing and reliability early warning (strew) metric suite
A software testing and reliability early warning (strew) metric suite
An empirical study into class testability
Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
Information and Software Technology
Evolutionary testing of object-oriented software
Proceedings of the 2010 ACM Symposium on Applied Computing
On understanding laws, evolution, and conservation in the large-program life cycle
Journal of Systems and Software
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One characteristic that impedes software from achieving good levels of maintainability is the increasing complexity of software. Empirical observations have shown that typically, the more complex the software is, the bigger the test suite is. Thence, a relevant question, which originated the main research topic of our work, has raised: "Is there a way to correlate the complexity of the test cases utilized to test a software product with the complexity of the software under test?". This work presents a new approach to infer software complexity with basis on the characteristics of automatically generated test cases. From these characteristics, we expect to create a test case profile for a software product, which will then be correlated to the complexity, as well as to other characteristics, of the software under test. This research is expected to provide developers and software architects with means to support and validate their decisions, as well as to observe the evolution of a software product during its life-cycle. Our work focuses on object-oriented software, and the corresponding test suites will be automatically generated through an emergent approach for creating test data named as Evolutionary Testing.