Aurora Trinidad Ramirez Pozo

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Software reliability models are used to estimate the probability of a software fails along the time. They are fundamental to plan test activities and to ensure the quality of the software being developed. Two kind of models are generally used: time or test coverage based models. In our previous work, we successfully explored genetic programming (GP) to(More)
Reliability models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to estimate the reliability growth, however, none of them has proven to perform well considering different project characteristics. In this work, we explore genetic programming (GP) as an alternative approach to(More)
This text presents the main results from the MOCAITO evaluation, related to the application of the quality indicators and comparison of the multiobjective algorithms. 1. Main Results Table 1 presents the main results obtained by each algorithm and system in Experiments 2M and 4M. Column 2 presents the cardinality of PFtrue, formed by the non-dominated(More)
Software testing is a fundamental software engineering activity for quality assurance that is also traditionally very expensive. To reduce efforts of testing strategies, some design metrics have been used to predict the fault-proneness of a software class or module. Recent works have explored the use of machine learning (ML) techniques for fault prediction.(More)
The adoption of probabilistic models for the best individuals found so far is a powerful approach for evolutionary computation. Increasingly more complex models have been used by estimation of distribution algorithms (EDAs), what often results better effectiveness on finding the global optima for hard optimization problems. Supervised and unsupervised(More)