@inproceedings{han-etal-2026-tdflow,
    title = "{TDF}low: Agentic Workflows for Test Driven Development",
    author = "Han, Kevin  and
      Maddikayala, Siddharth  and
      Knappe, Tim  and
      Patel, Om  and
      Liao, Austen  and
      Barati Farimani, Amir",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.70/",
    pages = "1511--1527",
    ISBN = "979-8-89176-380-7",
    abstract = "We introduce TDFlow, a novel test-driven agentic workflow that frames repository-scale software engineering as a test-resolution task, specifically designed to solve human-written tests. Given a set of tests, TDFlow repeatedly proposes, revises, and debugs repository-scale patches using precisely engineered sub-agents and tightly constrained tools. The workflow decomposes software engineering program repair into four components governed by respective sub-agents. This simple, forced decoupling of patch proposing, debugging, patch revision, and optional test generation (1) reduces long-context burden on any individual sub-agent, (2) focuses each sub-agent on specific, pre-defined sub-tasks, and (3) allows for specialized performance improvement on specific sub-tasks. When provided human-written tests, TDFlow attains 88.8{\%} pass rate on SWE-Bench Lite (an absolute improvement of 27.8{\%} over the next best baseline) and 94.3{\%} on SWE-Bench Verified. In this work, we further show that the primary obstacle to human-level software engineering performance lies within writing successful reproduction tests. Manual inspection of the 800 TDFlow runs within SWE-Bench Lite and Verified uncover only 7 instances of test hacking, which were subsequently counted as failures. We envision a human-LLM interactive system powered by TDFlow where human developers write tests solved by LLM systems. Together, these results show that modern LLMs, when embedded in a narrowly engineered, test-driven workflow, already achieve human-level test resolution {--} with the final frontier for fully autonomous repository repair being accurate reproduction test generation."
}