# Пов'язані статті щодо Software Engineering

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Software Engineering", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

First Long-Horizon Doc2Repo Training Dataset: Code Agents Move Beyond Bug Fixing and Begin Creating Repositories

With the advancement of LLM Code Agents, the research focus is shifting towards long-horizon, real-world tasks, moving beyond simple bug fixes to full repository generation. To address this, researchers from Renmin University of China introduced the DeNovoSWE dataset. This dataset focuses on long-term software engineering tasks, specifically the "document-to-repository" challenge—generating an entire, executable code repository from a task description. The DeNovoSWE construction method employs a Divide & Conquer approach. It breaks down target repositories into core capabilities and uses a multi-agent Draft-Critic-Repair workflow to automatically generate high-quality, evaluation-aligned task documents. The dataset also implements difficulty-aware filtering to balance quality and diversity. The result is a high-quality, anti-leakage dataset of 4,818 instances. Experiments show that models trained on DeNovoSWE achieve significant improvements in long-horizon repository generation. For instance, Qwen3-30B-A3B-Instruct's performance on the BeyondSWE-Doc2Repo benchmark increased from 5.8% to 47.2%, and on NL2RepoBench from 4.3% to 23.0%. Similar gains were observed with stronger backbones, demonstrating that dedicated long-horizon training data is crucial for advancing Code Agents from maintainers to architects capable of planning and building complete software projects from scratch.

marsbit06/25 08:51

First Long-Horizon Doc2Repo Training Dataset: Code Agents Move Beyond Bug Fixing and Begin Creating Repositories

marsbit06/25 08:51

What Should You Do First with Claude Fable 5? Give Your Code Repository a Comprehensive Checkup

Title: "What You Should Do First with Claude Fable 5: A Comprehensive Audit of Your Codebase" This article introduces a powerful use case for the newly released Claude Fable 5 AI model (June 2026), which is positioned for long-cycle software engineering tasks. It presents a detailed "Audit and Project Improvement" prompt template that transforms the AI from a mere code-writing assistant into a systematic "engineering audit and project improvement collaborator." The core recommendation is to apply this prompt to important code repositories. The prompt guides the AI, acting as a world-class principal engineer, through a rigorous four-stage audit process: 1. **Discovery & Mapping:** Systematically explore the repository to understand its structure, tech stack, purpose, and existing conventions before forming conclusions. 2. **Evidence-Based Audit:** Critically examine specific dimensions—architecture, code quality, security, testing, performance, dependencies, devops, and documentation—citing concrete file paths and line numbers for each finding, and rating their severity. 3. **Improvement Strategy:** Synthesize audit findings into 3-5 key thematic issues, propose target states with underlying principles, and define measurable completion criteria. 4. **Detailed Task Plan:** Break down the strategy into actionable tasks with titles, affected areas, acceptance criteria, effort estimates (S/M/L/XL), risk assessment, and dependencies. Tasks are organized into prioritized milestones (Security Net, Critical Fixes, High-Leverage Improvements, Quality Polish) and quick wins are highlighted. The final output is a consolidated report including an Executive Summary with a health grade, the Repo Map, Audit Report, Improvement Strategy, Task Plan, and Open Questions for human decision-makers. The prompt emphasizes evidence over speculation, respects project maturity, and focuses analysis on the core 20% of the codebase.

marsbit06/10 03:58

What Should You Do First with Claude Fable 5? Give Your Code Repository a Comprehensive Checkup

marsbit06/10 03:58

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