Maliheh Izadi

Assistant Professor, Delft University of Technology, The Netherlands.

prof_pic.jpg

TU Delft, The Netherlands

m[DOT]izadi[AT]tudelft[DOT]nl

I am an assistant professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at Delft University of Technology (TU Delft), the Netherlands.

At TU Delft, I direct the AI-enabled Software Engineering (AISE) research lab, where I supervise various PhD, MSc, and BSc students. I am also the academic manager for TU Delft’s tenth ICAI lab, AI4SE, consisted of 10 PhDs and various MSc/BSc students. Lastly, I am also part of the Software Engineering Research Group (SERG) at TU Delft.

My research is at the intersection of deep learning and source code analysis. I study, design, and develop learning-based models, specifically Large Language Models (#LLM) for source code, to automate software engineering and developer-related tasks such as understanding, generating, and documenting source code. My work has been published by several top venues in the software engineering community such as IEEE/ACM ICSE, FSE, TOSEM, EMSE, MSR, ICSME, SANER, and JSS.

I hold a PhD in Software Engineering from Sharif University of Technology (SUT). My PhD research focused on mining information from version control systems to facilitate software production-related tasks by automatically generating reports (such as release notes) for developers and software teams. I have a master’s degree in IT Engineering from SUT. My MSc research focused on improving evaluation methods of recommender systems which resulted in various publications.

If you are a researcher working on the intersection of NLP/ML and software engineering and you are interested in my work, feel free to reach out to propose/discuss possible collaborations. I have a postdoc position available on improving development tools and software engineering processes using #LLMs4code. Please apply through the portal for PhD vacancies or reach out to me through my email for the postdoc position. Make sure to introduce yourself, describe your research interests, and enclose a copy of your CV.

NOTE for prospective BSc and MSc students: If you are a TU Delft student who would like to work on the intersection of LLMs and SE in my lab, I have opportunities for a thesis, internships with industrial partners, and research projects. Feel free to contact me through my email to schedule a meeting and discuss potential opportunities.

Research Interests

  • Language models for source code
  • Evaluations and benchmarking
  • Autonmous agents
  • Intelligent development tools
  • Developer productivity

News

Apr 02, 2025 New paper on Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks accepted at the ACM FSE 2025 (Main Track).
Mar 14, 2025 New paper on Predictive Sequencing of States accepted at FSE 2025 (New Ideas and Vision Track).
Mar 10, 2025 New paper on Multi-agent Onboarding Assistant accepted at FSE 2025 (Demo Track).
Nov 07, 2015 A long announcement with details

Latest posts

Selected publications

  1. Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Enhancement Protocol
    Roham Koohestani, Philippe Bekker, and Maliheh Izadi
    2025
  2. Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks
    Ali Al-kaswan, Sebastian Deatc, Begum Koc, Arie Deursen , and 1 more author
    In 2025 ACM International Conference on the Foundations of Software Engineering (FSE), 2025
  3. Long code arena: a set of benchmarks for long-context code models
    Egor Bogomolov, Aleksandra Eliseeva, Timur Galimzyanov, Evgeniy Glukhov , and 7 more authors
    arXiv preprint arXiv:2406.11612, 2025
  4. Leveraging large language models for enhancing the understandability of generated unit tests
    Amirhossein Deljouyi, Roham Koohestani, Maliheh Izadi, and Andy Zaidman
    In 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE), 2025
  5. HyperSeq: A Hyper-Adaptive Representation for Predictive Sequencing of States
    Roham Koohestani, and Maliheh Izadi
    In 2025 ACM International Conference on the Foundations of Software Engineering (FSE), 2025
  6. Creativity, Generative AI, and Software Development: A Research Agenda
    Victoria Jackson, Bogdan Vasilescu, Daniel Russo, Paul Ralph , and 6 more authors
    2024
  7. A transformer-based approach for smart invocation of automatic code completion
    Aral Moor, Arie Deursen, and Maliheh Izadi
    2024
  8. Language models for code completion: A practical evaluation
    Maliheh Izadi, Jonathan Katzy, Tim Van Dam, Marc Otten , and 2 more authors
    In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE), 2024
  9. Traces of memorisation in large language models for code
    Ali Al-Kaswan, Maliheh Izadi, and Arie Van Deursen
    In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE), 2024
  10. Semantically-enhanced topic recommendation systems for software projects
    Maliheh Izadi, Mahtab Nejati, and Abbas Heydarnoori
    Empirical Software Engineering, 2023
  11. Predicting the objective and priority of issue reports in software repositories
    Maliheh Izadi, Kiana Akbari, and Abbas Heydarnoori
    Empirical Software Engineering, 2022
  12. Topic recommendation for software repositories using multi-label classification algorithms
    Maliheh Izadi, Abbas Heydarnoori, and Georgios Gousios
    Empirical Software Engineering, 2021