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  • Sorokin L, Safin D and Nejati S. (2025). Can search-based testing with pareto optimization effectively cover failure-revealing test inputs?. Empirical Software Engineering. 30:1. Online publication date: 1-Feb-2025.

    https://doi.org/10.1007/s10664-024-10564-3

  • Giamattei L, Biagiola M, Pietrantuono R, Russo S and Tonella P. (2025). Reinforcement learning for online testing of autonomous driving systems: a replication and extension study. Empirical Software Engineering. 30:1. Online publication date: 1-Feb-2025.

    https://doi.org/10.1007/s10664-024-10562-5

  • Huang L, Sun W, Yan M, Liu Z, Lei Y and Lo D. (2024). Neuron Semantic-Guided Test Generation for Deep Neural Networks Fuzzing. ACM Transactions on Software Engineering and Methodology. 34:1. (1-38). Online publication date: 31-Jan-2025.

    https://doi.org/10.1145/3688835

  • Arcaini P and Cetinkaya A. (2024). CRAG – a combinatorial testing-based generator of road geometries for ADS testing. Science of Computer Programming. 238:C. Online publication date: 1-Dec-2024.

    https://doi.org/10.1016/j.scico.2024.103171

  • Humeniuk D, Khomh F and Antoniol G. (2024). Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing. ACM Transactions on Software Engineering and Methodology. 33:8. (1-45). Online publication date: 30-Nov-2024.

    https://doi.org/10.1145/3680468

  • Tang S, Zhang Z, Zhou J, Lei L, Zhou Y and Xue Y. LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models. Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering. (1497-1508).

    https://doi.org/10.1145/3691620.3695520

  • Humeniuk D, Ben Braiek H, Reid T and Khomh F. In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators. Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering. (2187-2198).

    https://doi.org/10.1145/3691620.3695281

  • Li Z, Dai J, Huang Z, You N, Zhang Y and Yang M. VioHawk: Detecting Traffic Violations of Autonomous Driving Systems through Criticality-Guided Simulation Testing. Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis. (844-855).

    https://doi.org/10.1145/3650212.3680325

  • Jiang Z, Li H, Wang R, Tian X, Liang C, Yan F, Zhang J and Liu Z. (2024). Validity Matters: Uncertainty‐Guided Testing of Deep Neural Networks. Software Testing, Verification and Reliability. 10.1002/stvr.1894.

    https://onlinelibrary.wiley.com/doi/10.1002/stvr.1894

  • Biagiola M and Tonella P. (2024). Boundary State Generation for Testing and Improvement of Autonomous Driving Systems. IEEE Transactions on Software Engineering. 50:8. (2040-2053). Online publication date: 1-Aug-2024.

    https://doi.org/10.1109/TSE.2024.3420816

  • Zohdinasab T, Riccio V and Tonella P. (2024). Focused Test Generation for Autonomous Driving Systems. ACM Transactions on Software Engineering and Methodology. 33:6. (1-32). Online publication date: 31-Jul-2024.

    https://doi.org/10.1145/3664605

  • Aghababaeyan Z, Abdellatif M, Dadkhah M and Briand L. (2024). DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks. ACM Transactions on Software Engineering and Methodology. 33:6. (1-29). Online publication date: 31-Jul-2024.

    https://doi.org/10.1145/3644388

  • Duong H, Xu D, Nguyen T and Dwyer M. (2024). Harnessing Neuron Stability to Improve DNN Verification. Proceedings of the ACM on Software Engineering. 1:FSE. (859-881). Online publication date: 12-Jul-2024.

    https://doi.org/10.1145/3643765

  • Song D, Xie X, Song J, Zhu D, Huang Y, Juefei-Xu F and Ma L. (2024). LUNA: A Model-Based Universal Analysis Framework for Large Language Models. IEEE Transactions on Software Engineering. 50:7. (1921-1948). Online publication date: 1-Jul-2024.

    https://doi.org/10.1109/TSE.2024.3411928

  • Biagiola M, Stocco A, Riccio V and Tonella P. (2024). Two is better than one: digital siblings to improve autonomous driving testing. Empirical Software Engineering. 29:4. Online publication date: 1-Jul-2024.

    https://doi.org/10.1007/s10664-024-10458-4

  • Neelofar N and Aleti A. (2024). Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features. ACM Transactions on Software Engineering and Methodology. 33:4. (1-32). Online publication date: 31-May-2024.

    https://doi.org/10.1145/3640335

  • Doreste A, Biagiola M and Tonella P. (2024). Adversarial Testing with Reinforcement Learning: A Case Study on Autonomous Driving 2024 IEEE Conference on Software Testing, Verification and Validation (ICST). 10.1109/ICST60714.2024.00034. 979-8-3503-0818-1. (293-304).

    https://ieeexplore.ieee.org/document/10638566/

  • Zohdinasab T and Doreste A. DeepHyperion-UAV at the SBFT Tool Competition 2024 - CPS-UAV Test Case Generation Track. Proceedings of the 17th ACM/IEEE International Workshop on Search-Based and Fuzz Testing. (49-50).

    https://doi.org/10.1145/3643659.3648551

  • Biagiola M and Klikovits S. SBFT Tool Competition 2024 - Cyber-Physical Systems Track. Proceedings of the 17th ACM/IEEE International Workshop on Search-Based and Fuzz Testing. (33-36).

    https://doi.org/10.1145/3643659.3643932

  • Xiang Y, Huang H, Li S, Li M, Luo C and Yang X. (2023). Automated Test Suite Generation for Software Product Lines Based on Quality-Diversity Optimization. ACM Transactions on Software Engineering and Methodology. 33:2. (1-52). Online publication date: 29-Feb-2024.

    https://doi.org/10.1145/3628158

  • Zhi Y, Xie X, Shen C, Sun J, Zhang X and Guan X. (2023). Seed Selection for Testing Deep Neural Networks. ACM Transactions on Software Engineering and Methodology. 33:1. (1-33). Online publication date: 31-Jan-2024.

    https://doi.org/10.1145/3607190

  • Zohdinasab T, Riccio V and Tonella P. (2023). An Empirical Study on Low- and High-Level Explanations of Deep Learning Misbehaviours 2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). 10.1109/ESEM56168.2023.10304866. 978-1-6654-5223-6. (1-11).

    https://ieeexplore.ieee.org/document/10304866/

  • Arrieta A, Valle P, Iriarte A and Illarramendi M. (2023). How Do Deep Learning Faults Affect AI-Enabled Cyber-Physical Systems in Operation? A Preliminary Study Based on DeepCrime Mutation Operators 2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). 10.1109/ESEM56168.2023.10304794. 978-1-6654-5223-6. (1-7).

    https://ieeexplore.ieee.org/document/10304794/

  • Tang S, Zhang Z, Zhou J, Zhou Y, Li Y and Xue Y. (2023). EvoScenario: Integrating Road Structures into Critical Scenario Generation for Autonomous Driving System Testing 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE). 10.1109/ISSRE59848.2023.00054. 979-8-3503-1594-3. (309-320).

    https://ieeexplore.ieee.org/document/10301222/

  • Adigun J, Philip Huck T, Camilli M and Felderer M. (2023). Risk-driven Online Testing and Test Case Diversity Analysis for ML-enabled Critical Systems 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE). 10.1109/ISSRE59848.2023.00017. 979-8-3503-1594-3. (344-354).

    https://ieeexplore.ieee.org/document/10301271/

  • Huai Y, Almanee S, Chen Y, Wu X, Chen Q and Garcia J. (2023). <sc>scenoRITA</sc>: Generating Diverse, Fully Mutable, Test Scenarios for Autonomous Vehicle Planning. IEEE Transactions on Software Engineering. 49:10. (4656-4676). Online publication date: 1-Oct-2023.

    https://doi.org/10.1109/TSE.2023.3309610

  • Tang S, Zhang Z, Zhang Y, Zhou J, Guo Y, Liu S, Guo S, Li Y, Ma L, Xue Y and Liu Y. (2023). A Survey on Automated Driving System Testing: Landscapes and Trends. ACM Transactions on Software Engineering and Methodology. 32:5. (1-62). Online publication date: 30-Sep-2023.

    https://doi.org/10.1145/3579642

  • Fahmy H, Pastore F, Briand L and Stifter T. (2023). Simulator-based Explanation and Debugging of Hazard-triggering Events in DNN-based Safety-critical Systems. ACM Transactions on Software Engineering and Methodology. 32:4. (1-47). Online publication date: 31-Jul-2023.

    https://doi.org/10.1145/3569935

  • Dola S, Dwyer M and Soffa M. (2023). Input Distribution Coverage: Measuring Feature Interaction Adequacy in Neural Network Testing. ACM Transactions on Software Engineering and Methodology. 32:3. (1-48). Online publication date: 31-May-2023.

    https://doi.org/10.1145/3576040

  • Attaoui M, Fahmy H, Pastore F and Briand L. (2023). Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering. ACM Transactions on Software Engineering and Methodology. 32:3. (1-40). Online publication date: 31-May-2023.

    https://doi.org/10.1145/3550271

  • Riccio V and Tonella P. When and Why Test Generators for Deep Learning Produce Invalid Inputs: An Empirical Study. Proceedings of the 45th International Conference on Software Engineering. (1161-1173).

    https://doi.org/10.1109/ICSE48619.2023.00104

  • Aleti A. (2023). Software Testing of Generative AI Systems: Challenges and Opportunities 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE). 10.1109/ICSE-FoSE59343.2023.00009. 979-8-3503-2496-9. (4-14).

    https://ieeexplore.ieee.org/document/10449663/

  • Aghababaeyan Z, Abdellatif M, Briand L, S R and Bagherzadeh M. (2023). Black-Box Testing of Deep Neural Networks through Test Case Diversity. IEEE Transactions on Software Engineering. 49:5. (3182-3204). Online publication date: 1-May-2023.

    https://doi.org/10.1109/TSE.2023.3243522

  • Ferdous R, Hung C, Kifetew F, Prandi D and Susi A. (2023). EvoMBT at the SBFT 2023 Tool Competition 2023 IEEE/ACM International Workshop on Search-Based and Fuzz Testing (SBFT). 10.1109/SBFT59156.2023.00018. 979-8-3503-0182-3. (59-60).

    https://ieeexplore.ieee.org/document/10190390/

  • Biagiola M, Klikovits S, Peltomäki J and Riccio V. (2023). SBFT Tool Competition 2023 - Cyber-Physical Systems Track 2023 IEEE/ACM International Workshop on Search-Based and Fuzz Testing (SBFT). 10.1109/SBFT59156.2023.00010. 979-8-3503-0182-3. (45-48).

    https://ieeexplore.ieee.org/document/10190377/

  • Calsi D, Duran M, Zhang X, Arcaini P and Ishikawa F. (2023). Distributed Repair of Deep Neural Networks 2023 IEEE Conference on Software Testing, Verification and Validation (ICST). 10.1109/ICST57152.2023.00017. 978-1-6654-5666-1. (83-94).

    https://ieeexplore.ieee.org/document/10132206/

  • Zohdinasab T, Riccio V, Gambi A and Tonella P. (2023). Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems. ACM Transactions on Software Engineering and Methodology. 32:2. (1-38). Online publication date: 31-Mar-2023.

    https://doi.org/10.1145/3544792

  • Wei Z, Wang H, Ashraf I and Chan W. (2022). Predictive Mutation Analysis of Test Case Prioritization for Deep Neural Networks 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS). 10.1109/QRS57517.2022.00074. 978-1-6654-7704-8. (682-693).

    https://ieeexplore.ieee.org/document/10062402/

  • Stocco A, Nunes P, D'Amorim M and Tonella P. ThirdEye: Attention Maps for Safe Autonomous Driving Systems. Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. (1-12).

    https://doi.org/10.1145/3551349.3556968

  • Stocco A and Tonella P. (2021). Confidence‐driven weighted retraining for predicting safety‐critical failures in autonomous driving systems. Journal of Software: Evolution and Process. 10.1002/smr.2386. 34:10. Online publication date: 1-Oct-2022.

    https://onlinelibrary.wiley.com/doi/10.1002/smr.2386

  • Riccio V, Humbatova N, Jahangirova G and Tonella P. DeepMetis. Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering. (355-367).

    https://doi.org/10.1109/ASE51524.2021.9678764

  • Nguyen V, Huber S and Gambi A. (2021). SALVO: Automated Generation of Diversified Tests for Self-driving Cars from Existing Maps 2021 IEEE International Conference On Artificial Intelligence Testing (AITest). 10.1109/AITEST52744.2021.00033. 978-1-6654-3481-2. (128-135).

    https://ieeexplore.ieee.org/document/9564107/