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NEUROTESTGEN: Neuro-Symbolic Guided Test Generation with Large Language Models
2026 · 0 citations
Ensuring high structural coverage remains a fundamental challenge in automated test generation, particularly for complex software systems where reaching specific lines or branches requires satisfying intricate control- and data-flow constraints. Large Language Models (LLMs) have recently demonstrated strong capabilities in producing human-like test cases; however, they often struggle to generate inputs that satisfy precise path conditions. Conversely, symbolic execution can systematically derive…
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Why Don’t XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions
2022 · 39 citations
Machine Learning (ML) based defect prediction models can be used to improve the reliability and overall quality of software systems. However, such defect predictors might not be deployed in real applications due to the lack of transparency. Thus, recently, application of several post-hoc explanation methods (e.g., LIME and SHAP) have gained popularity. These explanation methods can offer insight by ranking features based on their importance in black box decisions. The explainability of ML techni…
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