Deep Learning for Code in the Agentic Era

Deep learning for code has progressed from focused tasks—such as completion, repair, synthesis, and explanation to tackling complex, end-to-end software–engineering problems. A key recent breakthrough is the rise of coding agents. Unlike single-shot models, these systems plan, reason, explore, and invoke external tools to assist throughout the software-development lifecycle: adding features, refactoring, debugging, finding vulnerabilities, optimizing performance, summarizing code, and answering repository-level questions. Their growing versatility demands rigorous evaluation and a deeper understanding of their capabilities, limits, risks, and broader social impact.

Building on momentum from both academia and industry (e.g. Google, OpenAI, Anthropic, SWE-Agent, OpenHands), we propose the 4th Deep Learning for Code (DL4C) workshop with a dedicated focus on coding agents. This workshop will provide a timely forum where researchers and practitioners can design and stress-test robust coding agents, discover novel applications and emergent behaviors, establish principled benchmarks and evaluation methods, study human–agent collaboration at scale, and advance the responsible, safe deployment of autonomous coding tools.