Percorrer por autor "Vega, Francisco Fernández de"
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- An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testingPublication . Ribeiro, José; Zenha-Rela, Mário Alberto; Vega, Francisco Fernández deThis paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.
- eCrash: a Genetic Programming-Based Testing Tool for Object-Oriented SoftwarePublication . Ribeiro, José Carlos Bregieiro; Nogueira, Ana Filipa; Vega, Francisco Fernández de; Zenha-Rela, Mário AlbertoThis paper describes the methodology, architecture and features of the eCrash framework, a Java-based tool which employs Strongly-Typed Genetic Programming to automate the generation of test data for the structural unit testing of Object-Oriented programs. The application of Evolutionary Algorithms to Test Data generation is often referred to as Evolutionary Testing. eCrash implements an Evolutionary Testing strategy developed with three major purposes: improving the level of performance and automation of the Software Testing process; minimising the interference of the tool’s users on the Test Object analysis to a minimum; and mitigating the impact of users decisions in the Test Data generation process.
- Enabling Object Reuse on Genetic Programming-Based Approaches to Object-Oriented Evolutionary TestingPublication . Ribeiro, José Carlos Bregieiro; Zenha-Rela, Mário Alberto; Vega, Francisco Fernández deRecent research on search-based test data generation for Object-Oriented software has relied heavily on typed Genetic Programming for representing and evolving test data. However, standard typed Genetic Programming approaches do not allow Object Reuse; this paper proposes a novel methodology to overcome this limitation. Object Reuse means that one instance can be passed to multiple methods as an argument, or multiple times to the same method as arguments. In the context of Object-Oriented Evolutionary Testing, it enables the generation of test programs that exercise structures of the software under test that would not be reachable otherwise. Additionally, the experimental studies performed show that the proposed methodology is able to effectively increase the performance of the test data generation process.