We propose a tool called Sen4Smells that performs an automated sensitivity analysis for a given debt index based on the evolution of both the index values and the corresponding smells across (past) system versions. Sen4Smells is designed as a pipeline that combines information from existing tools for smell detection, predefined debt index formulas, and the Sobol method for sensitivity analysis.
Architectural Smells & Machine Learning mash-up
Tool for predicting Dependency-based Architectural Smells
Tool for Prioritizing Architecture-Sensitive Smells based on a Technical Debt Index
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.