2023
Huang, W., Zhao, X., Banks, A., Cox, V., & Huang, X. (n.d.). Hierarchical Distribution-Aware Testing of Deep Learning. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3625290DOI: 10.1145/3625290
Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2023). Short-term Load Forecasting with Distributed Long Short-Term Memory. In 2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT. doi:10.1109/ISGT51731.2023.10066368DOI: 10.1109/ISGT51731.2023.10066368
The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments (Journal article)
Salako, K., & Zhao, X. (2023). The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments. IEEE Transactions on Software Engineering, 1-9. doi:10.1109/tse.2022.3233802
Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (2023). Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2023-May (pp. 1421-1429).
Salako, K., & Zhao, X. (n.d.). Demonstrating software reliability using possibly correlated tests: Insights from a conservative Bayesian approach. Quality and Reliability Engineering International. doi:10.1002/qre.3460DOI: 10.1002/qre.3460
2022
Dong, Y., Zhao, X., & Huang, X. (2022). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. In 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 5171-5178). doi:10.1109/IROS47612.2022.9981794DOI: 10.1109/IROS47612.2022.9981794
Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI COMMUNICATIONS, 35(4), 407-420. doi:10.3233/AIC-220128DOI: 10.3233/AIC-220128
Dong, Y., Zhao, X., & Huang, X. (2021). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. Retrieved from http://dx.doi.org/10.1109/IROS47612.2022.9981794
Qi, Y., Conmy, P. R., Huang, W., Zhao, X., & Huang, X. (2022). A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. In CEUR Workshop Proceedings Vol. 3215.
Bridging Formal Methods and Machine Learning with Global Optimisation (Chapter)
Huang, X., Ruan, W., Tang, Q., & Zhao, X. (2022). Bridging Formal Methods and Machine Learning with Global Optimisation. In Formal Methods and Software Engineering (pp. 1-19). Springer International Publishing. doi:10.1007/978-3-031-17244-1_1DOI: 10.1007/978-3-031-17244-1_1
2021
Dong, Y., Huang, W., Bharti, V., Cox, V., Banks, A., Wang, S., . . . Huang, X. (2023). Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 22(3). doi:10.1145/3570918
Zhao, X., Huang, W., Banks, A., Cox, V., Flynn, D., Schewe, S., & Huang, X. (2021). Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles. Retrieved from http://arxiv.org/abs/2106.01258v1
Salako, K., Strigini, L., & Zhao, X. (2021). Conservative Confidence Bounds in Safety, from Generalised Claims of Improvement & Statistical Evidence. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN 2021) (pp. 451-462). doi:10.1109/DSN48987.2021.00055DOI: 10.1109/DSN48987.2021.00055
Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J., & Huang, X. (n.d.). Coverage Guided Testing for Recurrent Neural Networks. IEEE Transactions on Reliability.
Zhao, X., Huang, W., Schewe, S., Dong, Y., & Huang, X. (2021). Detecting Operational Adversarial Examples for Reliable Deep Learning. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS - SUPPLEMENTAL VOL (DSN 2021) (pp. 5-6). doi:10.1109/DSN-S52858.2021.00013DOI: 10.1109/DSN-S52858.2021.00013
Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2021). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. In Proceedings of Machine Learning Research Vol. 161 (pp. 408-418).
2020
Zhao, X., Calinescu, R., Gerasimou, S., Robu, V., & Flynn, D. (2020). Interval Change-Point Detection for Runtime Probabilistic Model Checking. In 2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020) (pp. 163-174). doi:10.1145/3324884.3416565DOI: 10.1145/3324884.3416565
Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2020). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. Retrieved from http://arxiv.org/abs/2012.03058v5
Huang, W., Zhao, X., & Huang, X. (n.d.). Embedding and Extraction of Knowledge in Tree Ensemble Classifiers. Machine Learning. Retrieved from http://arxiv.org/abs/2010.08281v2
Assessing safety-critical systems from operational testing: A study on autonomous vehicles (Journal article)
Zhao, X., Salako, K., Strigini, L., Robu, V., & Flynn, D. (2020). Assessing safety-critical systems from operational testing: A study on autonomous vehicles. INFORMATION AND SOFTWARE TECHNOLOGY, 128. doi:10.1016/j.infsof.2020.106393DOI: 10.1016/j.infsof.2020.106393
On reliability assessment when a software-based system is replaced by a thought-to-be-better one (Journal article)
Littlewood, B., Salako, K., Strigini, L., & Zhao, X. (2020). On reliability assessment when a software-based system is replaced by a thought-to-be-better one. RELIABILITY ENGINEERING & SYSTEM SAFETY, 197. doi:10.1016/j.ress.2019.106752DOI: 10.1016/j.ress.2019.106752
Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M., & Huang, X. (2020). A Safety Framework for Critical Systems Utilising Deep Neural Networks. Retrieved from http://dx.doi.org/10.1007/978-3-030-54549-9_16
2019
Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J., & Huang, X. (2022). Coverage-Guided Testing for Recurrent Neural Networks. IEEE TRANSACTIONS ON RELIABILITY, 71(3), 1191-1206. doi:10.1109/TR.2021.3080664DOI: 10.1109/TR.2021.3080664
UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS (Conference Paper)
Osborne, M., Lantair, J., Shafiq, Z., Zhao, X., Robu, V., Flynn, D., & Perry, J. (2019). UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS. In 2019 4TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS 2019) (pp. 203-212). doi:10.1109/icsrs48664.2019.8987692DOI: 10.1109/icsrs48664.2019.8987692
The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation (Conference Paper)
Tang, W., Flynn, D., Brown, K., Valentin, R., & Zhao, X. (2019). The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation. In OCEANS 2019 MTS/IEEE SEATTLE. Retrieved from https://www.webofscience.com/
The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables (Conference Paper)
Tang, W., Flynn, D., Brown, K., Valentin, R., & Zhao, X. (2019). The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables. In OCEANS 2019 MTS/IEEE SEATTLE. Retrieved from https://www.webofscience.com/
Zhao, X., Osborne, M., Lantair, J., Robu, V., Flynn, D., Huang, X., . . . Ferrando, A. (2019). Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management. In SOFTWARE ENGINEERING AND FORMAL METHODS (SEFM 2019) Vol. 11724 (pp. 105-124). doi:10.1007/978-3-030-30446-1_6DOI: 10.1007/978-3-030-30446-1_6
Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing (Conference Paper)
Zhao, X., Robu, V., Flynn, D., Salako, K., & Strigini, L. (2019). Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing. In 2019 IEEE 30TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE) (pp. 13-23). doi:10.1109/ISSRE.2019.00012DOI: 10.1109/ISSRE.2019.00012
Machine learning methods for wind turbine condition monitoring: A review (Journal article)
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., . . . Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. RENEWABLE ENERGY, 133, 620-635. doi:10.1016/j.renene.2018.10.047DOI: 10.1016/j.renene.2018.10.047
2018
Zhao, X., Robu, V., Flynn, D., Dinmohammadi, F., Fisher, M., & Webster, M. (2019). Probabilistic Model Checking of Robots Deployed in Extreme Environments. In THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE (pp. 8066-8074). Retrieved from https://www.webofscience.com/
Fisher, M., Collins, E., Dennis, L., Luckcuck, M., Webster, M., Jump, M., . . . Zhao, X. (2018). Verifiable Self-Certifying Autonomous Systems. In 2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE. doi:10.1109/issrew.2018.00028DOI: 10.1109/issrew.2018.00028
Conservative claims for the probability of perfection of a software-based system using operational experience of previous similar systems (Journal article)
Zhao, X., Littlewood, B., Povyakalo, A., Strigini, L., & Wright, D. (2018). Conservative claims for the probability of perfection of a software-based system using operational experience of previous similar systems. RELIABILITY ENGINEERING & SYSTEM SAFETY, 175, 265-282. doi:10.1016/j.ress.2018.03.032DOI: 10.1016/j.ress.2018.03.032
2017
Modeling the probability of failure on demand (<i>pfd</i>) of a 1-out-of-2 system in which one channel is "quasi-perfect" (Journal article)
Zhao, X., Littlewood, B., Povyakalo, A., Strigini, L., & Wright, D. (2017). Modeling the probability of failure on demand (<i>pfd</i>) of a 1-out-of-2 system in which one channel is "quasi-perfect". RELIABILITY ENGINEERING & SYSTEM SAFETY, 158, 230-245. doi:10.1016/j.ress.2016.09.002DOI: 10.1016/j.ress.2016.09.002
2016
Conservative Claims about the Probability of Perfection of Software-based Systems (Conference Paper)
Zhao, X., Littlewood, B., Povyakalo, A., & Wright, D. (2015). Conservative Claims about the Probability of Perfection of Software-based Systems. In 2015 IEEE 26TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE) (pp. 130-140). Retrieved from https://www.webofscience.com/
2014
A Dependability Case Construction Approach Based on Dependability Deviation Analysis (Journal article)
Zhang, D. J., Lu, M. Y., & Zhao, X. Y. (n.d.). A Dependability Case Construction Approach Based on Dependability Deviation Analysis. Applied Mechanics and Materials, 543-547, 3682-3687. doi:10.4028/www.scientific.net/amm.543-547.3682DOI: 10.4028/www.scientific.net/amm.543-547.3682
2012
A New Approach to Assessment of Confidence in Assurance Cases (Conference Paper)
Zhao, X., Zhang, D., Lu, M., & Zeng, F. (2012). A New Approach to Assessment of Confidence in Assurance Cases. In Unknown Conference (pp. 79-91). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33675-1_7DOI: 10.1007/978-3-642-33675-1_7
2011
Variance Analysis Based Software Fault Localization (Conference Paper)
Variance Analysis Based Software Fault Localization (2011). In Unknown Conference (pp. 333-339). ASME Press. doi:10.1115/1.859858.paper46DOI: 10.1115/1.859858.paper46