Aligning Academia with Industry: An Empirical Study of Industrial Needs and Academic Capabilities in AI-Driven Software Engineering

• URL: http://arxiv.org/abs/2512.15148v1
• Date: Wed, 17 Dec 2025 07:29:18 GMT
• Title: Aligning Academia with Industry: An Empirical Study of Industrial Needs and Academic Capabilities in AI-Driven Software Engineering
• Authors: Hang Yu, Yuzhou Lai, Li Zhang, Xiaoli Lian, Fang Liu, Yanrui Dong, Ting Zhang, Zhi Jin, David Lo,
• Abstract summary: The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE)<n>While top-tier SE venues continue to show sustained or emerging focus on areas like automated testing and program repair, the alignment of these academic advances with real industrial needs remains unclear.<n>This study aims to refocus academic attention on these important yet under-explored problems and to guide future SE research toward greater industrial impact.
• Score: 45.09204791294318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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