Publications

Z. Aghababaeyan, M. Abdellatif, M. Dadkhah, and L. Briand, "DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks," ACM Transactions on Software Engineering and Methodology, 2024, Preprint: arXiv:2303.04878.

S. Sharifi, D. Shin, L. Briand, and N. Aschbacher, "Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search," IEEE Transactions on Software Engineering, 2023, Preprint: arXiv:2301.13807.

J. Lee, S. Shin, L. Briand, S. Nejati, "Probabilistic Safe WCET Estimation for Weakly Hard Real-Time Systems at Design Stages," ACM Transactions on Software Engineering and Methodology, 2023, Preprint: arXiv:2302.10288

A. Zolfagharian, M. Abdellatif, L. Briand, M. Bagherzadeh and Ramesh S, "A Search-Based Testing Approach for Deep Reinforcement Learning Agents", IEEE Transactions on Software Engineering, 2023, doi: 10.1109/TSE.2023.3269804.

N. Bayati Chaleshtari, F. Pastore, A. Goknil, and L. Briand, "Metamorphic Testing for Web System Security", IEEE Transactions on Software Engineering, 2023, Preprint,  doi: 10.1109/TSE.2023.3256322.

Ch. Boufaied, C. Menghi, D. Bianculli, and L. Briand, "Trace Diagnostics for Signal-based Temporal Properties", IEEE Transactions on Software Engineering, 2023, doi: 10.1109/TSE.2023.3242588

Z. Aghababaeyan, M. Abdellatif, L. Briand, Ramesh S, and M. Bagherzadeh, "Black-Box Testing of Deep Neural Networks through Test Case Diversity", IEEE Transactions on Software Engineering, 2023, doi: 10.1109/TSE.2023.3243522.

S. Fatima, T. A. Ghaleb and L. Briand, "Flakify: A Black-Box, Language Model-Based Predictor for Flaky Tests", IEEE Transactions on Software Engineering, 2022,  doi: 10.1109/TSE.2022.3201209.

A. S. Yaraghi, M. Bagherzadeh, N. Kahani and L. Briand, "Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts", IEEE Transactions on Software Engineering, 2022,  doi: 10.1109/TSE.2022.3184842.

R. Pan, M. Bagherzadeh, T.A. Ghaleb and L. Briand, "Test case selection and prioritization using machine learning: a systematic literature review", Empirical Software Engineering (Springer) , 27 (2), 1-43, 2022, doi:10.1007/s10664-021-10066-6 

M. Bagherzadeh, N. Kahani and L. Briand, "Reinforcement learning for test case prioritization", IEEE Transactions on Software Engineering, 2021, doi: 10.1109/TSE.2021.3070549 

J. Lee, E. Vigano, O Cornejo, F. Pastore, L. Briand, "Fuzzing for CPS Mutation Testing," in IEEE/ACM ASE, 2023, arXiv:2308.07949. 

R. Pan, T.A. Ghaleb, and L. Briand, "ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search," in ACM/IEEE ICSE, 2023, arXiv:2210.16269.

F. U. Haq, D. Shin, and L. Briand, "Many-objective reinforcement learning for online testing of DNN-enabled systems," in ACM/IEEE ICSE, 2023, arXiv:2210.15432.

F. U. Haq, D. Shin, and L. Briand, "Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization," in ACM/IEEE ICSE, 2022, doi:10.1145/3510003.3510188.

N. Bayati Chaleshtari, Y. Marquer, F. Pastore, and L. Briand, "AIM: Automated Input Set Minimization for Metamorphic Security Testing," in arXiv, 2024, arxiv:2402.10773.

A. S. Yaraghi, D. Holden, N. Kahani, L. Briand, "Automated Test Case Repair Using Language Models," in arXiv, 2024, arxiv:2401.06765.

R. Pan, T.A. Ghaleb, and L. Briand, "LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models," in arXiv, 2023, arXiv:2304.01397.

F. Hadadi, J.H. Dawes, D. Shin, D. Bianculli, and L. Briand, "Systematic Evaluation of Deep Learning Models for Failure Prediction," in arXiv, 2023, arXiv:2303.07230.

S. Fatima, H. Hemmati, and L. Briand, "FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair," in arXiv, 2023, arXiv:2307.00012.

S. Ali, C. Boufaied, D. Bianculli, P. Branco, L. Briand, and N. Aschbacher , "An Empirical Study on Log-based Anomaly Detection Using Machine Learning," in arXiv, 2023, arXiv:2307.16714.

A. Abbasishahkoo, M. Dadkhah, L. Briand, and D. Lin, "TEASMA: A Practical Approach for the Test Assessment of Deep Neural Networks using Mutation Analysis," in arXiv, 2023, arXiv:2308.01311.

A. Zolfagharian, M. Abdellatif, L. Briand, and Ramesh S, "SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents," in arXiv, 2023, arXiv:2308.02594.