Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM

no code implementations • 31 Jan 2024 • Gabriel Ryan, Siddhartha Jain, Mingyue Shang, Shiqi Wang, Xiaofei Ma, Murali Krishna Ramanathan, Baishakhi Ray

Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance.

Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks (Extended Version)

1 code implementation • 17 Mar 2020 • Jianan Yao, Gabriel Ryan, Justin Wong, Suman Jana, Ronghui Gu

In this paper, we introduce a new neural architecture for general SMT learning, the Gated Continuous Logic Network (G-CLN), and apply it to nonlinear loop invariant learning.

CLN2INV: Learning Loop Invariants with Continuous Logic Networks

1 code implementation • ICLR 2020 • Gabriel Ryan, Justin Wong, Jianan Yao, Ronghui Gu, Suman Jana

We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset.

Fine Grained Dataflow Tracking with Proximal Gradients

no code implementations • 8 Sep 2019 • Gabriel Ryan, Abhishek Shah, Dongdong She, Koustubha Bhat, Suman Jana

Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel information leak detection.

Cryptography and Security