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 the 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 allows the comparison of different machine learning techniques will be developed. Analysis of possible improvements derived from the introduction of subjectivity, adaptability, and grayscale is also an objective of the work.
keywords: Design Smell, Detection Tools, Machine Learning, Software Quality, Empirical Study