3 papers
cs.SE2026
Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
Giulian Biolo, Michael Tezza, Yuanjun Gong +1
Software vulnerability remediation is a cognitively demanding task that requires specialized security expertise often lacking in general developers. In the meantime, Large Language…
cs.SE2026
LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments
Maria Camporese, Fabio Massacci, Yuanjun Gong
[Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because reliable annotations require m…
cs.SE2026
Feature Slice Matching for Precise Bug Detection
Ke Ma, Jianjun Huang, Wei You +4
Measuring the function similarity to detect bugs is effective, but the statements unrelated to the bugs can impede the performance due to the noise interference. Suppressing the no…