Communication is one of the most demanding andimportant parts of effective software development.Furthermore, the effectiveness of software developmentcommunication can be measured with the three collaborativeinterpersonal problem-solving conversation dimensions:Active Discussion, Creative Conflict, and ConversationManagement.Previous work that utilized these dimensions to analyzecommunication relied on manually labeling thecommunication, a process that is time-consuming and notapplicable to real-time use.In this study, natural language processing and supervisedmachine learning were investigated for the automaticclassification and measurement of collaborativeinterpersonal problem-solving conversation dimensions intranscribed software development communication. Thisapproach enables the evaluation of communication andprovides suggestions to improve software developmentefficiency.To determine the optimal classification approach, this workexamined nine different classifiers. It was determined thatthe classifier that scored the highest was Random Forest,followed by Decision Tree and SVM.Random Forest managed to achieve accuracy, precision, andrecall up to 93.66%, 93.76%, and 93.63%, respectively whentrained and tested with stratified 10-fold cross-validation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:miun-48994 |
| Date | January 2023 |
| Creators | Haapasaari Lindgren, Marcus, Persson, Jon |
| Publisher | Mittuniversitetet, Institutionen för kommunikation, kvalitetsteknik och informationssystem (2023-) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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