Publications

An up-to-date list of my publications:

Journals (ISI)

  • [18] Parsa, S., Zakeri-Nasrabadi, M., & Turhan, B. (2025). Testability-driven development: An improvement to the TDD efficiency. Computer Standards & Interfaces, 91, 103877.

  • [17] Ghaffari, O. B., Yekta, B. E., & Zakeri-Nasrabadi, M. (2024). Designing high-performance ion-exchangeable glasses with multi-objective optimization and machine learning. Ceramics International, 50(21), 42949-42962.

  • [16] Zakeri-Nasrabadi, M., Parsa, S., & Jafari, S. (2024). Measuring and improving software testability at the design level. Information and Software Technology, 174, 107511.

  • [15] Majidzadeh, A., Ashtiani, M., & Zakeri-Nasrabadi, M. (2024). Multi-type requirements traceability prediction by code data augmentation and fine-tuning MS-CodeBERT. Computer Standards & Interfaces, 90, 103850.

  • [14] Mir, M., Nasirzadeh, F., Zakeri, M., Hill, A., & Karmakar, C. (2024). Assessing neural markers of attention during exposure to construction noise using machine learning classification of electroencephalogram data. Building and Environment, 261, 111754.

  • [13] Golmohammadi, R., Parsa, S., & Zakeri-Nasrabadi, M. (2024). Dynamic domain testing with multi-agent Markov chain Monte Carlo method. Soft Computing, 28(13), 8293-8317.

  • [12] Zakeri-Nasrabadi, M., & Parsa, S. (2024). Natural language requirements testability measurement based on requirement smells. Neural Computing and Applications, 36(21), 13051-13085.

  • [11] Ghaffari, O. B., Yekta, B. E., & Zakeri-Nasrabadi, M. (2024). Estimating “depth of layer”(DOL) in ion-exchanged glasses using explainable machine learning. Materialia, 33, 102027.

  • [10] Ardalani, A., Parsa, S., Zakeri-Nasrabadi, M., & Chatzigeorgiou, A. (2024). Supporting single responsibility through automated extract method refactoring. Empirical Software Engineering, 29(1), 28.

  • [9] Zarepour, E., Mohammadi, M. R., Zakeri-Nasrabadi, M., Aein, S., Sangsari, R., Taheri, L., ... & Zabihallahpour, A. (2024). BiliBin: An Intelligent Mobile Phone-based Platform to Monitor Newborn Jaundice. Iranian Journal of Electrical & Electronic Engineering, 20(3).

  • [8] Zakeri-Nasrabadi, M., Parsa, S., Ramezani, M. , Roy C. K., & Ekhtiarzadeh, M., (2023). A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges Journal of Systems and Software, 111796. https://doi.org/10.1016/j.jss.2023.111796

  • [7] Zakeri-Nasrabadi, M., Parsa, S., Esmaili, E., & Palomba, F. (2023). A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms. Journal of Computer Languages, 74, 101177. https://dl.acm.org/doi/abs/10.1145/3596908

  • [6] Parsa, S., Zakeri-Nasrabadi, M., Ekhtiarzadeh, M., & Ramezani, M. (2023). Method name recommendation based on source code metrics. Journal of Computer Languages, 74, 101177. https://doi.org/10.1016/j.cola.2022.101177

  • [5] Zakeri-Nasrabadi, M., & Parsa, S. (2022). An ensemble meta-estimator to predict source code testability. Applied Soft Computing, 129, 109562. https://doi.org/10.1016/j.asoc.2022.109562

  • [4] Shahidi, M., Ashtiani, M., & Zakeri-Nasrabadi, M. (2022). An automated extract method refactoring approach to correct the long method code smell. Journal of Systems and Software, 187, 111221. https://doi.org/10.1016/j.jss.2022.111221

  • [3] Zakeri-Nasrabadi, M, Parsa, S. (2021). Learning to predict test effectiveness. IInternational Journal of Intelligent Systems, 37: 4363- 4392. https://doi.org/10.1002/int.22722

  • [2] Zakeri-Nasrabadi, M., Tabibi, H., Salmani, M., Torkashvand, M., & Zarepour, E. (2021). A comprehensive survey on non-invasive wearable bladder volume monitoring systems. Medical & Biological Engineering & Computing, 59(7), 1373-1402. https://doi.org/10.1007/s11517-021-02395-x

  • [1] Zakeri-Nasrabadi, M., Parsa, S., & Kalaee, A. (2021). Format-aware learn&fuzz: deep test data generation for efficient fuzzing. Neural Computing and Applications, 33(5), 1497-1513. https://doi.org/10.1007/s00521-020-05039-7

Conferences

  • [4] Hashemifar, S., & Zakeri-Nasrabadi, M. (2024, February). Deep Identification of Plant Diseases. In 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP'24) (pp. 1-6). IEEE.

  • [3] Majidzadeh, A., Ashtiani, M., & Zakeri-Nasrabadi, M. (2023). Code data augmentation to improve language model's performance in requirement to code traceability link recovery. In Proceedings of the 9th International Conference on Web Research, Tehran, 2023. University of Science and Culture. https://civilica.com/doc/1672065/

  • [2] Zakeri-Nasrabadi, M. , & Parsa, S. (2021). Learning to predict software testability. In 26th International Computer Conference, Computer Society of Iran. Tehran: IEEE. https://doi.org/10.1109/CSICC52343.2021.9420548

  • [1] Zakeri-Nasrabadi, Z., & Zakeri-Nasrabadi, M. (2019). Analysis social phenomena using machine learning techniques: a mixed research framework. In The first conference on artificial intelligence and soft computing in humanities (AISCH-2019). Retrieved from http://aisch.atu.ac.ir/paper?manu=106226

Journals (ISC)

  • [2] Zakeri-Nasrabadi, M., Parsa, S., & Hayati, Z. (2023). Automatic test data generation to improve fault-localization based on causal-statistical analysis. Journal of Soft Computing and Information Technology, 12(3), 74-84 (In Persian). Retrieved from https://jscit.nit.ac.ir/article_188522.html

  • [1] Zakeri-Nasrabadi, M., & Parsa, S. (2020). Automatic Test Data Generation in File Format Fuzzers. Electronic and Cyber Defense, 8(1), 1-16. (In Persian). Retrieved from https://ecdj.ihu.ac.ir/article_204735_en.html

Theses

Ph.D. dissertation

New (September 2022): An early view of my Ph.D. dissertation online appendix is now available at https://m-zakeri.github.io/PhD.

Master thesis

In my M.Sc. thesis, I designed and built IUST-DeepFuzz, a file format fuzzer and provided IUST-PDFCorpus, a large dataset of PDF files and PDF data objects. IUST-DeepFuzz can automatically learn the grammar (structure) of a given input file, then generate and fuzz various test data based on the learned model and some mutation-based methods. You can find all relevant information about my M.Sc. thesis on the IUST-DeepFuzz GitHub repository.

Bachelor project

In my B.Sc. project, I worked on agent-oriented software engineering and developed a multi-agent system to participate in the multi-agent programming contest (MAPC). Unfortunately, the competitions did not hold in the year 2014, for the technical reasons raised by the new scenario, and our team could not participate in the competitions. However, MAPC is alive for me and my teammates. Hence, our final project reports is kept in draft version to be updated ASAP:)

  • My B.Sc. Project Report (draft version) [www]

Find more on IUST course materials.

Research profiles