Detecting Technical Debt through Issue Trackers

Managing technical debt effectively to prevent it from accumulating too quickly is of great concern to software stakeholders. To pay off technical debt regularly, software developers must be conscious of the existence of technical debt items. The first step is to make technical debt explicit; that is the identification of technical debt. Although there exist many kinds of static source code analysis tools to identify code-level technical debt, identifying non-code-level technical debt is very challenging and needs deep exploration. This paper proposed an approach to identifying non-code-level technical debt through issue tracking data sets using natural language processing and machine learning techniques and validated the feasibility and performance of this approach using an issue tracking data set recorded in Chinese from a commercial software project. We found that there are actually some common words that can be used as indicators of technical debt. Based on these key words, we achieved the precision of 0.72 and the recall of 0.81 for identifying technical debt items using machine learning techniques respectively. Keywords—technical debt; identification; issue tracking data sets; natural language processing; machine learning