This paper presents DebtHunter, a natural language processing (NLP)- and machine learning (ML)- based approach for identifying and classifying SATD in source code comments. The proposed classification approach combines two classification phases for differentiating between the multiple debt types.
Architectural Smells & Machine Learning mash-up
Tool for predicting Dependency-based Architectural Smells
The goal of this work is toshed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressionsin social media texts.
Architectural Smells & Machine Learning mash-up
We explore a forward-looking approach that is able to infer groups of likely module dependencies that can anticipate architectural smells in a future system version.