arXiv:2607. 16564v1 Announce Type: cross Abstract: Background: Evaluating automated Software Requirements Specification (SRS) generation is challenging because few datasets provide fine-grained traceability between source requirements, intermediate elicitation artifacts, and generated specifications.
arXiv:2607. 14896v1 Announce Type: cross Abstract: Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report.
The paper introduces ADFD‑Migrate, a method that extracts a latent declarative representation of code—an annotated data‑flow diagram (ADFD)—to aid large‑scale repository migration. By using an LLM to infer the source ADFD from repository context and guiding target‑language generation with dependency‑aware chunking, the approach improves porting soundness and completeness. Evaluated on 50 Fortran repositories, the system achieves high behavioral agreement and a superior migration outcome index compared to baseline translation methods.
SA-Bench is a diagnostic benchmark that evaluates how well large language model (LLM) agents reproduce scientific papers by measuring semantic alignment between generated code and the papers’ specifications. It covers 30 recent machine‑learning papers, decomposing each into 1,491 Semantic Alignment Units (SAUs) that assess numerical, methodological, protocol, and ordering fidelity. Across 12 generator configurations, the best model (Claude+PaperCoder) scores only 0.301 on average, highlighting that current LLMs often implement requirements incorrectly or with stubs.
arXiv:2608. 05204v1 Announce Type: new Abstract: LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workflows.
ReproAgent is a four‑stage pipeline—Prepare, Plan, Generate, Repair—that uses a persistent implementation contract to guide scientific AI agents in converting research papers into executable code repositories. The system employs two channels: an implementation‑requirement channel that translates paper snippets into code obligations, and a reference‑evidence channel that pulls content and structure from related repositories. Evaluated on PaperBench Code‑Dev, ReproAgent achieves the highest mean score among same‑backbone scaffolds for both Claude‑Sonnet‑4.5 and Gemini‑3‑Flash, with ablation studies confirming the contribution of both channels.
The paper evaluates a deterministic supersession memory, MemStrata, for retrieval‑augmented generation (RAG) systems on real software history. Using 707 GitHub issues, the authors extracted 130 clean atomic state transitions where a single value changes from pre‑fix to post‑fix. MemStrata achieved 0.91 answer accuracy versus 0.57–0.59 for standard RAG, eliminating stale‑fact errors that RAG returned 36–38% of the time, while maintaining comparable retrieval latency.
REFINE is a tool-agnostic, evidence-aware multi-agent approach that generates Java file-level refactoring candidates by combining static analysis, smell-informed planning, LLM-based transformation, and automated re-analysis. In experiments on 450 Java files from 15 open-source systems, REFINE reduced detected code smells by 68–73% across three LLM configurations, achieving higher median reductions with smaller edits compared to a direct-prompt baseline. However, the tool’s outputs still pose risks such as assert/fail-call changes and public-method removal, requiring compilation, testing, dependency analysis, and human review before deployment.
arXiv:2608. 04278v1 Announce Type: cross Abstract: Coding agents increasingly work across sessions, but prose notes can preserve a conclusion without the program state that supported it.
arXiv:2607. 02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.
arXiv:2604. 03447v2 Announce Type: replace-cross Abstract: LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mutually inconsistent.
arXiv:2606. 12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents.
arXiv:2607. 02615v2 Announce Type: replace-cross Abstract: Generating structured artifacts with Large Language Models - e.