MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis
2026-07-29 • Software Engineering
Software EngineeringComputation and LanguageMachine Learning
AI summaryⓘ
The authors developed MindForge, a system that turns existing open-source command-line programs into training environments for AI coding agents, focusing on creating complete programs from scratch. They used MindForge to generate training data and fine-tuned a large language model, Qwen3.6-27B, which improved its ability to write and modify code across various software tasks. This fine-tuning led to better performance on multiple challenging benchmarks compared to the base model and matched results of much bigger models. Overall, their approach helps address the difficulty of teaching AI to write entire programs by providing a more comprehensive training environment.
program synthesiscode generationsoftware engineering benchmarksAI fine-tuningtraining environmentprogram compilationlanguage modelsbug fixingfeature implementationcross-language code translation
Authors
Yihao Chen, Shi Chang, Khaled Chawa, Feng Lin, Boyuan Chen, Shaowei Wang, Ahmed E. Hassan
Abstract
Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation. However, constructing a complete program from scratch remains a major challenge: even the frontier models evaluated on ProgramBench fully resolve fewer than 1% of tasks. One obstacle is the lack of scalable training environments for this from-scratch setting, spanning the whole software engineering life cycle, as existing environment-construction frameworks focus only on a single phase in software development. To address this gap, we introduce MindForge, an automated pipeline that converts open-source command-line programs into source-free environments that expose only a compiled reference executable and its documentation. Using MindForge, we construct training environments from repositories disjoint from those in ProgramBench, and curate a high-quality data recipe consisting of program synthesis trajectories using GLM-5.2 as the teacher agent. Fine-tuning Qwen3.6-27B on these trajectories increases its ProgramBench average test pass rate from 37.98% to 49.51%, achieving performance comparable to substantially larger frontier models. Moreover, the fine-tuned model consistently improves over the base model across all seven unseen software engineering benchmarks, spanning long-horizon repository generation and translation, bug fixing, feature implementation, and cross-language issue resolution, with absolute gains of 31.00 points on RepoZero-C2Rust, 14.16 on DeepSWE, 10.70/4.56 on NL2Repo-Bench (with/without tests), 5.04 on SWE-bench Verified, 5.93 on SWE-bench Pro, 5.22 on SWE-bench Multilingual, and 4.94 on FeatBench.