This presentation was part of the Seventh International Workshop on Managing Technical Debt, held in conjunction with the 31st International Conference on Software Maintenance and Evolution (ICSME 2015).
The identification of technical debt (TD) is an important step to effectively managing it. In this context, a set of indicators has been used by automated approaches to identify TD items, but some debt may not be directly identified using only metrics collected from the source code. In this work, we propose CVM-TD, a model to support the identification of technical debt through code comment analysis. We performed an exploratory study on two large, open sources projects with the goal of characterizing the feasibility of the proposed model to support the detection of TD through code comments analysis. The results indicate that (1) developers use the dimensions considered by CVM-TD when writing code comments, (2) CVM-TD provides a vocabulary that may be used to detect TD items, and (3) the proposed model needs to be calibrated to reduce the difference between comments returned by the vocabulary and those that may indicate a TD item. Code comments analysis can be used to detect TD in software projects and CVM-TD may support the development team to perform this task.