2012 MRes projects
- Twitter and Crime: The spatio-temporal link between social-media and criminal activity
- To what extent do water treatment processes affect the concentration of peroxide explosives in river water?
- Dual-band Frequency Reconfigurable Antennas
- Incorporating Nanostructures to Enhance the Performance of Semiconducting Metal
- A relevance study determining the use of GSR upon clothing and shoes as an item of evidence
- Automating the conceptual analysis of large-scale text-based subjective data sets
- Assessing the potential of e-noses for illicit drug detection in future drug-trafficking interdiction strategies
- Judgement in UK fingermark recovery: room for development?
- Modelling the allocation of crowd control resources
- Comparative study of the different feature extraction algorithms used for fingerprint identification
- Domain Adaptation of Statistical Classifiers for Security-related Bug Reports
- The detection of clandestine methamphetamine laboratories using semiconducting metal oxide gas sensors
- The evaluation of geochemical analysis methods for forensic provenance and interpretation
- Confirmation bias: A Study of biasability within Forensic anthropological visual assessments on skeletal remains
- Statistical change point detection of internet traffic
- Trace evidence dynamics: assessing the transfer and persistence of microbial diatom evidence in forensic investigation
- Data Communication for Underwater Sensor Networks
- Automated Cargo Inspection: Exploring the use of Machine Vision in X-ray Transmission Imaging
- Network Externalities and Migration: An Agent-Based Model Distinguishing Documented and Undocumented Flows
Domain Adaptation of Statistical Classifiers for Security-related Bug Reports
21 March 2013
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.