Improving Design Smell Detection for Adoption in Industry

TítuloImproving design smell detection for adoption in industry
Autor/aKhalid Alkharabsheh
DirectoresJosé Ángel Taboada González
TutoresJosé Ángel Taboada González
TipoTese doutoral
Data de lectura11/04/2019
Lugar de lecturaUniversidade de Santiago de Compostela

Programas científicos

TítuloImproving Design Smell Detection for Adoption in Industry
Autoreskhalid Alkharabsheh
TipoComunicación para congreso
Fonte 8th International Conference on Computer Science and Information Technology, Amman (Jordan), pp. 213-218 , 2018.
ISBN978-1-5386-4151-4
DOI10.1109/CSIT.2018.8486180
AbstractThis 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.
Palabras chaveDesign Smell, Detection Tools, Machine Learning, Software Quality, Empirical Study