Back
  1. Home
  2. Profile of Fitash UL HAQ (Unilu)
Profil

UL HAQ Fitash

Main Referenced Co-authors
BRIAND, Lionel  (5)
SHIN, Donghwan  (5)
NEJATI, Shiva  (2)
Iqbal, Muhammad Zohaib (1)
khan, Muhammad Uzair (1)
Main Referenced Keywords
DNN Testing (2); Testing (2); Automated Driving System (1); DADS testing (1); Deep Learning (1);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab) (3)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation (2)
Main Referenced Disciplines
Computer science (7)

Publications (total 7)

The most downloaded
878 downloads
Ul Haq, F., Shin, D., & Briand, L. (2022). Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 811-822). New York, NY, United States: ACM. doi:10.1145/3510003.3510188 https://hdl.handle.net/10993/50091

The most cited

26 citations (Scopus®)

Ul Haq, F., Shin, D., & Briand, L. (2022). Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 811-822). New York, NY, United States: ACM. doi:10.1145/3510003.3510188 https://hdl.handle.net/10993/50091

Ul Haq, F., Shin, D., & Briand, L. (2023). Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems. In 45th International Conference on Software Engineering (ICSE ’23). New York, NY, United States: ACM. doi:10.1109/ICSE48619.2023.00155
Peer reviewed

Ul Haq, F. (2022). SCALABLE AND PRACTICAL AUTOMATED TESTING OF DEEP LEARNING MODELS AND SYSTEMS [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/53011

Ul Haq, F., Shin, D., & Briand, L. (2022). Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 811-822). New York, NY, United States: ACM. doi:10.1145/3510003.3510188
Peer reviewed

Ul Haq, F., Shin, D., Nejati, S., & Briand, L. (05 July 2021). Can Offline Testing of Deep Neural Networks Replace Their Online Testing? Empirical Software Engineering, 26 (5). doi:10.1007/s10664-021-09982-4
Peer Reviewed verified by ORBi

Ul Haq, F., Shin, D., Briand, L., Stifter, T., & Wang, J. (2021). Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper). In 2021 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) (pp. 91-102).
Peer reviewed

Ul Haq, F., Shin, D., Nejati, S., & Briand, L. (2020). Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study. In 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). IEEE. doi:10.1109/ICST46399.2020.00019
Peer reviewed

Iqbal, M. Z., Sartaj, H., khan, M. U., Ul Haq, F., & Qaisar, I. (2019). A Model-Based Testing Approach for Cockpit Display Systems of Avionics. International Conference on Model Driven Engineering Languages and Systems. doi:10.1109/MODELS.2019.00-14
Peer reviewed

Contact ORBilu