AbstractsComputer Science

Improving Classification Results Using Class Imbalance Solutions & Evaluating the Generalizability of Rationale Extraction Techniques

by Tanmay Mathur

Institution: Miami University
Department: Computer Science
Degree: Master of Computer Science
Year: 2015
Keywords: Computer Science; rationale extraction; Chrome Bug Reports; SPSD
Record ID: 2059099
Full text PDF: http://rave.ohiolink.edu/etdc/view?acc_num=miami1420335486


During the software development process many decisions are made. The decisions, alternatives, and the reasons for and against those alternatives constitute the software design rationale. The research tries to improve the rationale classification results by use of new features. An improvement was observed only for rationale classification but other rationale types showed a F measure reduction. The research also evaluates the generalizability of the machine learning approach using a new dataset though does not establish it. The research implements and experiments with SMOTEBoost algorithm that is used in combination with the new features to give improved results for some categories of rationale. The thesis also describes a tool that can map classified outputs to the original instances that can be used to find missing features.