Ontological Evolutionary Encoding to Bridge Machine Learning and Conceptual Models: Approach and Industrial Evaluation

In this work, we propose an evolutionary ontological encoding approach to enable Machine Learning techniques to be used to perform Software Engineering tasks in models. The approach is based on a domain ontology to encode a model and on an Evolutionary Algorithm to optimize the encoding. As a result, the encoded model that is returned by the approach can then be used by Machine Learning techniques to perform Software Engineering tasks such as concept location, traceability link retrieval, reuse, impact analysis, etc. We have evaluated the approach with an industrial case study to recover the traceability link between the requirements and the models through a Machine Learning technique (RankBoost). Our results in terms of recall, precision, and the combination of both (F-measure) show that our approach outperforms the baseline (Latent Semantic Indexing). We also performed a statistical analysis to assess the magnitude of the improvement.