Melhoria de processos de software através da combinação de proveniência de dados, ontologias, redes complexas e visualizações

Abstract

Software development process is a complex activity, which is influenced by many factors and can be surprised by an unexpected software behavior. Software‟s importance has grown exponentially in the past few years, which makes software improvement extremely necessary, as it is present in many different aspects of daily life. Analyze data from previous executions may be a good tactic to deal with software unpredictability, and to record processes‟ data is necessary to implement software monitoring and control. The present work proposes an architecture that encompasses provenance data, ontology and complex network models to structure data provenance in software process‟ domain and allow implicit knowledge extraction. The architecture proposed has a visualization layer to support project managers‟ data comprehension, allowing them to have data-oriented decision making and improve future process executions. The proposed architecture was evaluated with real companies‟ data and through a study with a specialist participation.