Skip to content
Conferences
- Mir, A. M., Keshani, M., & Proksch, S. (2024, April). On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study. In 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR) (pp. 457-468). IEEE.
- Venkatesh, A. P. S., Sabu, S., Mir, A. M., Reis, S., & Bodden, E. (2024, April). The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks. In Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering (pp. 35-39).
- Shivarpatna Venkatesh, A. P., Sabu, S., Wang, J., M. Mir, A., Li, L., & Bodden, E. (2024, April). TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools. In Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (pp. 49-53).
- A. M. Mir, M. Keshani, and S. Proksch. On the effect of transitivity and granularity on vulnerability propagation in the maven ecosystem. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 201–211, March 2023
- Mir, A. M., Latoškinas, E., Proksch, S., & Gousios, G., Type4Py: practical deep similarity learning-based type inference for Python. In Proceedings of the 44th International Conference on Software Engineering (ICSE’22), May 2022.
- A. Mir, E. Latoskinas and G. Gousios, “ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference,” in 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), May 2021
- A. Mir and Jalal A. Nasiri. Automatic opinion mining of movie reviews using robust twin support vector machine. In 4th Iranian Conference on Computational Linguistics. Institute for Humanities and Cultural Studies, February 2018 [in Persian]
- A. Mir, Somayeh Fatahi, and Jalal A. Nasiri. Prediction of personality models in e-learning environments using twin support vector machine. In 2nd International Conference on Knowledge-Based Research in Computer Engineering and Information Technology. Allameh Tabataba’i University, September 2017 [in Persian]
- A. Mir, Jalal A. Nasiri, and Somayeh Fatahi. Sentiment analysis of movie reviews using least squares twin support vector machine. In 1st Conference on Participles of Electrical and Computer Engineering. Payame Noor University, July 2017 ] [in Persian]
Journals
- Nasiri, J. A., & Mir, A. M. (2020). An enhanced KNN-based twin support vector machine with stable learning rules. Neural Computing and Applications, 1-21.
- Mir, A., & Nasiri, J. A. (2018). KNN-based least squares twin support vector machine for pattern classification. Applied Intelligence, 48(12), 4551-4564.
- Mir, A. M., & Nasiri, J. A. (2019). LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier. Journal of Open Source Software, 4, 1252.
- Nasiri, J., Mir, A. M., & Fatahi, S. (2019). Classification of learning styles using behavioral features and twin support vector machine. Technology of Education Journal (TEJ) [in Persian]
Pre-prints
- Mir, A. M., Keshani, M., & Proksch, S. (2024). OriginPruner: Leveraging Method Origins for Guided Call Graph Pruning. arXiv preprint arXiv:2412.09110.
- Mir, A. M., Rahbar, M., & Nasiri, J. A. (2020). LIBTwinSVM: A Library for Twin Support Vector Machines. arXiv preprint arXiv:2001.10073.
Theses