Abstract
Technical Debt (TD) can be detected using different methods. TD is a metaphor that refers to short-term solutions in software development, which may affect the cost of the software development life-cycle. Several tools have been developed to detect, estimate, or manage TD. TD can be indicated through smells, code comments, and software metrics. Machine learning Techniques (MLTs) are used in many software engineering topics such as fault-proneness, bug severity, and code smell. In this paper we use four internal structure metrics to identify and classify Architecture Technical Debt (ATD) risk by using MLTs. We show that MLTs can identify and classify the risk of ATD on software components to help the decision-makers to prioritizing the refactoring decisions based on the level of the risk.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
