Clarify and test language-agnostic pipeline robustness across long-tail ecosystems

Determine a precise operational definition of "language-agnostic" for automated pipelines that construct executable repository-level software engineering tasks with test-based verification, and ascertain whether such pipelines remain robust across long-tail programming-language toolchains and diverse repository conventions.

Background

The paper targets scalable, reproducible environments for training software engineering agents across diverse languages. Prior systems are often evaluated on a small set of ecosystems, and the authors note ambiguity in what practically constitutes a language-agnostic construction pipeline.

This uncertainty is coupled with concerns about robustness for repositories using less common toolchains and non-standard conventions, which is critical for training-focused research that requires large, diverse, and reliable executable tasks.

References

First, it is unclear what it means for a construction pipeline to be language-agnostic in practice: many systems are evaluated on a small number of ecosystems, leaving open questions about robustness to long-tail toolchains and repository conventions.

— SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale  (2602.23866 - Badertdinov et al., 27 Feb 2026) in Section 1 (Introduction)