A black-box LLM approach for fault localization in system-level test code that estimates execution traces from failure logs to rank potential faults with reduced inference cost.
A3test: Assertion-augmented automated test case generation,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.SE 2verdicts
UNVERDICTED 2representative citing papers
GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.
citing papers explorer
-
Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models
A black-box LLM approach for fault localization in system-level test code that estimates execution traces from failure logs to rank potential faults with reduced inference cost.
-
Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.