Recommending when Design Technical Debt Should be Self-Admitted

Previous research has shown how developers "selfadmit" technical debt introduced in the source code, commenting why such code represents a workaround or a temporary, incomplete solution. This paper investigates the extent to which previously self-admitted technical debt can be used to provide recommendations to developers when they write new source code, suggesting them when to "self-admit" design technical debt, or possibly when to improve the code being written. To achieve this goal, we have developed a machine learning approach named TEDIOUS (TEchnical Debt IdentificatiOn System), which leverages various kinds of method-level features as independent variables, including source code structural metrics, readability metrics and, last but not least, warnings raised by static analysis tools. We assessed TEDIOUS on data from nine open source projects for which there are available tagged self-admitted technical debt instances, also comparing the performances of different machine learners. Results of the study indicate that TEDIOUS achieves, when recommending self-admitted technical debts within a single project, an average precision of about 50% and a recall of 52%. When predicting cross-projects, TEDIOUS improves, achieving an average precision of 67% and a recall of 55%. Last, but not least, we noticed how TEDIOUS leverages readability, size and complexity metrics, as well as some warnings raised by static analysis tools.