Genetic algorithm-based program synthesizer for the construction of machine learning pipelines

Because of the growing presence of artificial intelligence, developers are looking for more efficient methods to construct machine learning algorithms. Program synthesizers allow us to produce algorithms consisting of scalers, feature selection and classifiers. Each of these pipelines is a potential solution to the given machine learning task....

Guided Metamorphic Transformations for Testing the Robustness of Trained Code2Vec Models

Machine learning models are increasingly being used within software engineering for their predictions. Research shows that these models’ performance is increasing with new research. This thesis focuses on models for method name prediction, for which the goal is to have a model that can accurately predict method names. With this thesis, we could...

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...

A search-based training algorithm for cost-aware defect prediction

Research has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each others' code changes. Developers are unlikely to devote the same effort to inspect each software artifact predicted...