The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE), driving a paradigm shift in both academic research and industrial practice. While top-tier SE venues continue to show sustained or emerging focus on areas like automated testing and program repair, with researchers worldwide reporting continuous performance gains, the alignment of these academic advances with real industrial needs remains unclear. To bridge this gap, we first conduct a systematic analysis of 1,367 papers published in FSE, ASE, and ICSE between 2022 and 2025, identifying key research topics, commonly used benchmarks, industrial relevance, and open-source availability. We then carry out an empirical survey across 17 organizations, collecting 282 responses on six prominent topics, i.e., program analysis, automated testing, code generation/completion, issue resolution, pre-trained code models, and dependency management, through structured questionnaires. By contrasting academic capabilities with industrial feedback, we derive seven critical implications, highlighting under-addressed challenges in software requirements and architecture, the reliability and explainability of intelligent SE approaches, input assumptions in academic research, practical evaluation tensions, and ethical considerations. This study aims to refocus academic attention on these important yet under-explored problems and to guide future SE research toward greater industrial impact.

翻译：大型语言模型（LLMs）的快速发展正在从根本上重塑软件工程（SE），推动学术研究与工业实践的双重范式转变。尽管顶级SE会议持续或新兴地关注自动化测试与程序修复等领域，全球研究人员报告了持续的性能提升，但这些学术进展与真实工业需求的对齐情况仍不明确。为弥合这一差距，我们首先对2022年至2025年间在FSE、ASE和ICSE会议上发表的1,367篇论文进行了系统分析，识别出关键研究主题、常用基准、工业相关性及开源可用性。随后，我们在17家组织间开展了一项实证调查，通过结构化问卷收集了关于六个重要主题（即程序分析、自动化测试、代码生成/补全、问题解决、预训练代码模型和依赖管理）的282份反馈。通过对比学术能力与工业反馈，我们得出七项关键启示，突显了软件需求与架构中未被充分应对的挑战、智能SE方法的可靠性与可解释性、学术研究中的输入假设、实际评估中的矛盾以及伦理考量。本研究旨在重新聚焦学术界对这些重要但未充分探索的问题的关注，并引导未来SE研究实现更大的工业影响力。