Cybersecurity, Software Documentation, and Machine Learning
Almost every aspect of our life is now dependent on analytics and services offered by diverse data-driven intelligent software systems. Our decisions and quality of life can be influenced by the analytics and services offered by such data-driven software systems. As such, it is important that such software systems are designed and engineered properly so that they offer trustworthy analytics. We conduct research to design and develop trustworthy and secure intelligent software systems by combining software engineering techniques with machine learning, deep learning, natural language processing, social science, and human computer interaction techniques.
- Cybersecurity and Social Adoption of Trustworthy Analytics and Software
- Cyber software security and vulnerability analysis, monitoring, and testing.
- Trustworthiness analysis of online shared contents/code.
- Social adoption of trustworthy and secure analytics and software systems.
- Software Documentation
- Crowd-sourced software review and insight summarization.
- Automatic detection and monitoring of software documentation quality
- The automatic creation of better quality software documentation.
- Dependable Machine Learning Software Applications (MLSAs)
- Automatic testing of MLSAs
- Fairness/bias testing of MLSAs
- Adversarial Security Testing