3 citations · 5 across the 6 of their papers we have counts for
7 papers
LLM-based Code Auditing by Inferring and Evaluating Developer Assumptions
Junming Liu, Marcel Böhme
When we see a null pointer check, we know that the developer believes that the pointer may not be initialized. If the same pointer is later used without a null pointer check, we ma…
LLM-Based Agents for Identifying Bug-Introducing Commits
Niklas Risse, Marcel Böhme
Śliwerski, Zimmermann, and Zeller (SZZ) just won the 2026 ACM SIGSOFT Impact Award for asking: When do changes induce fixes? Their paper from 2005 served as the foundation for a wi…
Empirical Computation: Prompting versus Programming
Eric Tang, Jing Liu, Marcel Böhme
Large Language Models (LLM) can solve *any* computational problem *without* an algorithm in a runtime *independent* of the computational complexity of that problem. Instead of spec…
Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection
Niklas Risse, Jing Liu, Marcel Böhme
According to our survey of machine learning for vulnerability detection (ML4VD), 9 in every 10 papers published in the past five years define ML4VD as a function-level binary class…
Fundamental Challenges in Cybersecurity and a Philosophy of Vulnerability-Guided Hardening
Marcel Böhme
Research in cybersecurity may seem reactive, specific, ephemeral, and indeed ineffective. Despite decades of innovation in defense, even the most critical software systems turn out…
Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection
Niklas Risse, Marcel Böhme
Recent results of machine learning for automatic vulnerability detection (ML4VD) have been very promising. Given only the source code of a function , ML4VD techniques can decide…
MCPA: Program Analysis as Machine Learning
Marcel Böhme
Static program analysis today takes an analytical approach which is quite suitable for a well-scoped system. Data- and control-flow is taken into account. Special cases such as poi…