Content classification of developer emails
|Title||Content classification of developer emails|
|Publication Type||Conference Paper|
|Year of Publication||2012|
|Authors||Bacchelli, Alberto, Dal Sasso Tommaso, D'Ambros Marco, and Lanza Michele|
|Secondary Title||Proceedings of the 34th IEEE/ACM International Conference On Software Engineering (ICSE 2012)|
|Keywords||email, Emails, Empirical software engineering, mailing list, natural language, Unstructured Data Mining|
Emails related to the development of a software system contain information about design choices and issues encountered during the development process. Exploiting the knowledge embedded in emails with automatic tools is challenging, due to the unstructured, noisy and mixed language nature of this communication medium. Natural language text is often not well-formed and is interleaved with languages with other syntaxes, such as code or stack traces.
We present an approach to classify email content at line level. Our technique classifies email lines in five categories (i.e., text, junk, code, patch, and stack trace) to allow one to subsequently apply ad hoc analysis techniques for each category. We evaluated our approach on a statistically significant set of emails gathered from mailing lists of four unrelated open source systems.
We created a web application to manually classify email content in the chosen categories. We classified a statistically significant set of emails from four java open source software (OSS) systems, used to evaluate the accuracy of our approach.