SWE-Ext: Extending and Scaling Augmented Data for Repository-Level Coding Tasks

ICLR 2026 Conference Submission17965 Authors

Keywords: Code Generation, Large Language Models, Software Engineering

TL;DR: Extend the SWE dataset by adding multilingual tasks and completion tasks.

Abstract: Repository-level benchmarks such as SWE-Bench have highlighted the challenges of scaling language models to complex software engineering tasks. However, current training data remains narrow in scope, primarily focusing on monolingual issue resolving and feature implementation. In this work, we introduce SWE-Ext, a large-scale effort to extend and scale augmented data for repository-level coding tasks. SWE-Ext broadens existing data along two key dimensions: multilingual coverage (spanning 10 languages) and an auxiliary code completion task. We uncover distinct transfer mechanisms: data from other programming languages provides transferable signals that generally enhance localization and editing capabilities in single-language (Python) settings, while code completion data strengthens code editing capabilities, particularly for feature implementation tasks requiring substantial new code generation. These extensions yield consistent improvements on Python repository-level benchmarks like SWE-Bench and FEA-Bench. Our method offers a simple yet effective way to leverage more open-source data for advancing repository-level code models.

Primary Area: datasets and benchmarks

Submission Number: 17965