Utilization of Machine Learning for the detection of self-admitted vulnerabilities
Motivation: Technical debt is a metaphor that describes not-quite-right code introduced for short-term needs. Developers are aware of it and admit it in source code comments, which is called Self-Admitted Technical Debt (SATD). Therefore, SATD indicates weak code that developers are aware of. Problem statement: Inspecting source code is time-consuming; automatically inspecting source code for its vulnerabilities is a crucial aspect of developing software. As it helps practitioners to reduce the time-consuming process and focus on vulnerable source code. Proposal: To accurately identify and better understand the semantics of self-admitted technical debt (SATD). Additionally, leveraging NL-PL approaches to detect vulnerabilities and the related SATD. Finally, a CI/CD pipeline will be proposed to make the vulnerability discovery process easily accessible to practitioners.
Mon 11 DecDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
09:00 - 10:30 | Doctoral symposiumDoctoral Symposium at W303 Chair(s): Maria Teresa Baldassarre Department of Computer Science, University of Bari , Tommi Mikkonen University of Helsinki | ||
09:00 10mDay opening | Opening words Doctoral Symposium | ||
09:10 40mDoctoral symposium paper | Simulation-Based Safety Testing of Automated Driving Systems Doctoral Symposium | ||
09:50 40mDoctoral symposium paper | Utilization of Machine Learning for the detection of self-admitted vulnerabilities Doctoral Symposium Moritz Mock Free University of Bozen-Bolzano DOI |