67 citations · 299 across the 33 of their papers we have counts for
9 papers · 1 filter
Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification
Xi Zheng, Dulanga Weerakoon, Yintong Huo +8
Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on discussions from the AAAI'26 Br…
CONCUR: Benchmarking LLMs for Concurrent Code Generation
Jue Huang, Tarek Mahmud, Corina Pasareanu +1
Leveraging Large Language Models (LLMs) for code generation has increasingly emerged as a common practice in the domain of software engineering. Relevant benchmarks have been estab…
Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Daniel Koh, Yannic Noller, Corina S. Pasareanu +2
Large language models (LLMs) have demonstrated strong performance on coding tasks such as generation, completion and repair, but their ability to handle complex symbolic reasoning…
Enhancing LLM Code Generation with Ensembles: A Similarity-Based Selection Approach
Tarek Mahmud, Bin Duan, Corina Pasareanu +1
Ensemble learning has been widely used in machine learning to improve model robustness, accuracy, and generalization, but has not yet been applied to code generation tasks with lar…
Agentic AI Software Engineers: Programming with Trust
Abhik Roychoudhury, Corina Pasareanu, Michael Pradel +1
Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (A…
SkipFuzz: Active Learning-based Input Selection for Fuzzing Deep Learning Libraries
Hong Jin Kang, Pattarakrit Rattanukul, Stefanus Agus Haryono +4
Many modern software systems are enabled by deep learning libraries such as TensorFlow and PyTorch. As deep learning is now prevalent, the security of deep learning libraries is a…
An Overview of Structural Coverage Metrics for Testing Neural Networks
Muhammad Usman, Youcheng Sun, Divya Gopinath +3
Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios…
Symbolic Execution for Deep Neural Networks
Divya Gopinath, Kaiyuan Wang, Mengshi Zhang +2
Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns. This paper introduces DeepCheck, a new ap…
Badger: Complexity Analysis with Fuzzing and Symbolic Execution
Yannic Noller, Rody Kersten, Corina S. Păsăreanu
Hybrid testing approaches that involve fuzz testing and symbolic execution have shown promising results in achieving high code coverage, uncovering subtle errors and vulnerabilitie…