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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Development emails content analyzer: intention mining in developer discussions |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | IEEE/ACM International Conference on Automated Software Engineering |
| Event Type | conference |
| Event Location | Lincoln, Nebraska, USA |
| Event Start Date | November 9 - 2015 |
| Event End Date | November 13 - 2015 |
| Publisher | s.n. |
| Abstract Text | Written development communication (e.g. mailing lists, issue trackers) constitutes a precious source of information to build recommenders for software engineers, for example aimed at suggesting experts, or at redocumenting existing source code. In this paper we propose a novel, semi-supervised approach named DECA (Development Emails Content Analyzer) that uses Natural Language Parsing to classify the content of development emails according to their purpose (e.g. feature request, opinion asking, problem discovery, solution proposal, information giving etc), identifying email elements that can be used for specific tasks. A study based on data from Qt and Ubuntu, highlights a high precision (90%) and recall (70%) of DECA in classifying email content, outperforming traditional machine learning strategies. Moreover, we successfully used DECA for re-documenting source code of Eclipse and Lucene, improving the recall, while keeping high precision, of a previous approach based on ad-hoc heuristics. |
| Zusammenfassung | , Sebastiano Panichella, Corrado Aaron Visaggio, , Gerardo Canfora and Harald Gall |
| Official URL | http://ase2015.unl.edu/ |
| Digital Object Identifier | 10.1109/ASE.2015.12 |
| Other Identification Number | merlin-id:12395 |
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