BC-Bench is a new benchmark for assessing agentic engineering systems on the domain‑specific language AL used in Microsoft Dynamics 365 Business Central. It contains 101 manually curated tasks drawn from real Microsoft production repositories and evaluates not only code generation but also test creation and multimodal problem statements. The study shows that performance gaps between models are larger than those between different agent harnesses, and that gains on general‑purpose benchmarks do not reliably transfer to the AL domain.
By Haoran Sun, Klaus Marius Hansen
arXiv:2606. 04967v1 Announce Type: cross Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification.
By Sanderson Oliveira de Macedo
The paper introduces Spec-Driven Agentic Development (SDAD), a framework that leverages large language models to ingest extensive functional requirement documents and repository context in a single workflow, turning specification quality into the engine for autonomous software delivery. SDAD blends disciplined upfront formalisation with rapid implementation, encompassing intent capture, machine‑readable specifications, agentic synthesis, and multi‑agent verification with human sign‑off. It positions AI‑code as a fourth production paradigm, compares it to traditional Waterfall and Agile approaches, and extends the model to team role evolution, quantitative governance metrics, and a staged migration blueprint for practical adoption.
By Vu Hung Nguyen, Thanh Nguyen
arXiv:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.
By Ruiyin Li, Yiran Zhang, Xiyu Zhou, Yangxiao Cai, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu
arXiv:2606. 05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
By Zhenfeng Cao
arXiv:2607. 01087v1 Announce Type: cross Abstract: Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production.
By James C. Davis, Paschal C. Amusuo, Tanmay Singla, Berk \c{C}akar, Kirsten A. Davis
arXiv:2607. 10856v1 Announce Type: cross Abstract: The rise of Software Engineering (SE) agents, i.
By Yunbo Lyu, David Williams, Jieke Shi, Zhensu Sun, Chao Peng, Zhou Yang, Federica Sarro, David Lo
arXiv:2608. 12133v1 Announce Type: new Abstract: Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images.
By Shivali Dalmia, Sumukha Thoppanahalli, Mohammadreza Sediqin, Abhishek Mukherji
arXiv:2607. 14456v1 Announce Type: cross Abstract: Large Language Models (LLMs) have accelerated the adoption of software development agents, now widely available as Integrated Development Environment (IDE) extensions and standalone applications.
By Harris Borman, Herman Wandabwa, Fusun Yu, Sandeepa Kannangara, Justin Liu, Anna Leontjeva, Ritchie Ng
arXiv:2606. 05608v2 Announce Type: replace-cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
By Zhenfeng Cao
arXiv:2608. 15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve.
By Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge, Ashmita Kapoor, Tanya Dixit
WiseSpec is a requirements‑driven agent framework designed to improve repository‑level code generation. It automatically builds structured, information‑rich requirements, evaluates their quality via execution‑based tests, and iteratively refines them to better guide code generation. Experiments show WiseSpec outperforms all baselines, achieving an average 13.17% improvement in %Resolved.
By Zhao Tian