arXiv:2602. 22456v2 Announce Type: replace-cross Abstract: Requirements are inherently interconnected through various types of dependencies.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2604. 22207v2 Announce Type: replace-cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate their generation and processing.
arXiv:2607. 04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development.
HoarePrompt is a new method that applies program verification concepts to natural language requirements, using large language models to generate step‑by‑step natural language descriptions of program states. It incorporates a few‑shot k‑induction technique to handle loops and then evaluates whether the annotated program satisfies the requirements. On the CoCoClaNeL dataset, HoarePrompt raises the Matthews correlation coefficient by 61% over zero‑shot chain‑of‑thought prompts and by 106% over test‑generation classifiers, with the inductive reasoning component adding a 26% MCC improvement.
arXiv:2606. 03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data.
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.
The paper introduces DA-Cramming, a cost‑effective pretraining method that incorporates dependency agreement information into BERT‑style language models. It builds on the Cramming technique to enable training with a single GPU in a day, using a dual‑stage workflow and four submodels to embed chunk‑level dependency agreements. Experiments show that this approach outperforms prior methods on a range of tasks.