Improving Design Smell Detection for Adoption in Industry

This work deals with the development of a classification algorithm that will improve the usefulness of design smell detection tools for its adoption in industry in order to increase software quality (maintainability, understandability, etc.). The current knowledge of Design Smell Detection (types of smells, approaches, strategies, algorithms, tools, etc.) will be identified. After that, a dataset that allow the comparison of different machine learning techniques will be developed. Analysis of possible improvements derived of the introduction of subjectivity, adaptability and gray scale are also an objective of the work.