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Domain Adaptation of Statistical Classifiers for Security-related Bug Reports

21 March 2013


Ziyu Wang

In open source software, fixing bugs depends on efforts from both public and original developers. Bug report is a text document describing details of errors or mistakes in software. It is necessary to classify the bug reports generated during this process in order to protect the security related bugs from being exploited by hackers. Compared with manual classification process, automatic classification saves time and resources.  Bug reports for different software may have different format, structure and meanings. Therefore, a classifier trained from certain database of bug reports may have low classification accuracy when applied to data from another software environment. This is regarded as a domain adaptation problem. Thus we would like to adapt a classifier to be able to test different software’s bug reports while maintaining good performance. This project aims to design a classification system with domain adaptation approach to increase the classification accuracy by adapting a trained classifier to the testing environment.