@inproceedings{saadi-etal-2025-nala,
    title = "{NALA}{\_}{MAINZ} at {BLP}-2025 Task 2: A Multi-agent Approach for {B}engali Instruction to Python Code Generation",
    author = "Saadi, Hossain Shaikh  and
      Alam, Faria  and
      Sanz-Guerrero, Mario  and
      Bui, Minh Duc  and
      Mager, Manuel  and
      von der Wense, Katharina",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.banglalp-1.61/",
    pages = "601--607",
    ISBN = "979-8-89176-314-2",
    abstract = "This paper presents JGU Mainz{'}s winning system for the BLP-2025 Shared Task on Code Generation from Bangla Instructions. We propose a multi-agent-based pipeline. First, a code-generation agent produces an initial solution from the input instruction. The candidate program is then executed against the provided unit tests (pytest-style, assert-based). Only the failing cases are forwarded to a debugger agent, which reruns the tests, extracts error traces, and, conditioning on the error messages, the current program, and the relevant test cases, generates a revised solution. Using this approach, our submission achieved first place in the shared task with a Pass@1 score of 95.4. We also make our code public."
}