Muhammad Usman

According to our database, Muhammad Usman authored at least 14 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

An overview of structural coverage metrics for testing neural networks.

Muhammad Usman, Youcheng Sun, Divya Gopinath, Rishi Dange, Luca Manolache, Corina S. Pasareanu

Int. J. Softw. Tools Technol. Transf., June, 2023

Rule-Based Testing of Neural Networks.

Muhammad Usman, Youcheng Sun, Divya Gopinath, Corina S. Pasareanu

Proceedings of the 1st International Workshop on Dependability and Trustworthiness of Safety-Critical Systems with Machine Learned Components, 2023

2022

AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks.

Muhammad Usman, Youcheng Sun, Divya Gopinath, Corina S. Pasareanu

CoRR, 2022

Rule-Based Runtime Mitigation Against Poison Attacks on Neural Networks.

Muhammad Usman, Divya Gopinath, Youcheng Sun, Corina S. Pasareanu

Proceedings of the Runtime Verification - 22nd International Conference, 2022

VPN: Verification of Poisoning in Neural Networks.

Youcheng Sun, Muhammad Usman, Divya Gopinath, Corina S. Pasareanu

Proceedings of the Software Verification and Formal Methods for ML-Enabled Autonomous Systems, 2022

2021

QuantifyML: How Good is my Machine Learning Model?

Muhammad Usman, Divya Gopinath, Corina S. Pasareanu

Proceedings of the Proceedings Third Workshop on Formal Methods for Autonomous Systems, 2021

NEUROSPF: A Tool for the Symbolic Analysis of Neural Networks.

Muhammad Usman, Yannic Noller, Corina S. Pasareanu, Youcheng Sun, Divya Gopinath

Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering: Companion Proceedings, 2021

NNrepair: Constraint-Based Repair of Neural Network Classifiers.

Muhammad Usman, Divya Gopinath, Youcheng Sun, Yannic Noller, Corina S. Pasareanu

Proceedings of the Computer Aided Verification - 33rd International Conference, 2021

2020

A study of learning likely data structure properties using machine learning models.

Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Cagdas Yelen, Nima Dini, Sarfraz Khurshid

Int. J. Softw. Tools Technol. Transf., 2020

A Study of Symmetry Breaking Predicates and Model Counting.

Wenxi Wang, Muhammad Usman, Alyas Almaawi, Kaiyuan Wang, Kuldeep S. Meel, Sarfraz Khurshid

Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems, 2020

A study of the learnability of relational properties: model counting meets machine learning (MCML).

Muhammad Usman, Wenxi Wang, Marko Vasic, Kaiyuan Wang, Haris Vikalo, Sarfraz Khurshid

Proceedings of the 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2020

TestMC: Testing Model Counters using Differential and Metamorphic Testing.

Muhammad Usman, Wenxi Wang, Sarfraz Khurshid

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020

2019

A Study of the Learnability of Relational Properties (Model Counting Meets Machine Learning).

Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Marko Vasic, Haris Vikalo, Sarfraz Khurshid

CoRR, 2019

A Study of Learning Data Structure Invariants Using Off-the-shelf Tools.

Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Cagdas Yelen, Nima Dini, Sarfraz Khurshid

Proceedings of the Model Checking Software - 26th International Symposium, 2019