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Machine Learning for Program Analysis
Program analysis is an interdisciplinary topic encompassing programming languages, formal methods, and software engineering, with the overarching goal to build algorithms and methods to find unwanted program behaviors like vulnerabilities and bugs. Program analysis techniques are not perfect, and their shortcomings have limited their impact on modern programs. Over the past years, machine learning has shown enormous potential in solving many complex tasks, drawing the attention of the program analysis community and, hopefully, program analysis. The research community has started investigating applications of machine learning to program analysis tasks, and this seminar intends to explore this nascent research area.
This seminar covers research papers addressing key challenges at the intersection of these two fields. How do we represent code for ML models? What tasks can ML solve? Can we use classifiers as vulnerability detectors? This seminar will explore these research questions and more.
In this seminar, students learn to work independently on an assigned single topic consisting of multiple papers. Each week, one or two students present a paper on the topic of interest, followed by a discussion of the main paper. All participants are expected to participate in the discussion by asking questions actively. Moreover, students are required to send questions about the main paper the day before the seminar.
- Kickoff meeting, 26/10
- Regular sessions on Wednesdays, 12:00-14:00
- First on 09/11/2022
- Last on 08/02/2023
- Seminar paper deadlines:
- Draft submission deadline, TBD
- Final submission deadline, TBD
Topics and Assignments
See this page.