A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008)

Abstract

Software quality is defined as the degree to which a software component or system meets specified requirements and specifications. Objectively quantifying software quality is important as it reduces cost, effort and time. Nonetheless, assessment of software quality is a difficult task since most software quality characteristics such as maintainability, reliability and reusability cannot be directly measured. However, they can be predicted from other software quality attributes such as complexity and inheritance. Many metrics to measure these software quality attributes have been proposed. These metrics are used to build software quality estimation models that take different forms: statistical models, rule based models and decision trees. In our work, we will deal with rule based estimation models for two main reasons: First, their white box nature makes them easy to interpret by human experts. Second, they supply guidelines that clearly show how to reach the prediction. Since data used to build such estimation models is scarce in the software quality estimation field, the accuracy of these rule based models gravely deteriorates when applied on new unseen data. The goal of this thesis is to explore and assess the use of hybrid heuristics to improve and adapt the rule based models to the context specific datasets.