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论文编号：	15838
作者编号：	2320234062
上传时间：	2025/12/10 19:15:39
中文题目：	L公司基于AI的软件研发过程管理优化策略研究
英文题目：	The Research on Optimization Strategies for AI-Based Software Development Process Management in L Company
指导老师：	姚欣林
中文关键字：	人工智能；软件研发管理；生成式AI；研发效能；人机协同
英文关键字：	Artificial Intelligence； Software Development Management； Generative AI； R&D Efficiency； Human-Machine Collaboration
中文摘要：	随着人工智能技术的迅猛发展，其在软件研发全流程中的深度应用正推动软件工程管理模式发生根本性变革。本论文聚焦人工智能驱动下的软件研发过程管理优化问题，以中型产业互联网企业L公司为实证研究对象，旨在构建一套适配企业实际、可落地、可度量的AI赋能研发管理优化方案，助力企业提升研发效能、保障交付质量、控制项目成本。研究工作立足于理论与实践双重维度，系统梳理国内外AI4SE（人工智能赋能软件工程）领域的最新研究进展，结合L公司2025年上半年真实研发数据与管理现状，识别其在需求管理、任务分配、代码审查、测试自动化及运维交付等环节存在的效率瓶颈与质量风险。 论文采用文献研究法、问卷调查法、访谈法与统计分析法相结合的研究方法，首先构建“AI驱动的软件研发过程优化模型”理论框架，继而针对L公司具体痛点，从需求分析智能化、设计辅助自动化、开发编码协同化、测试生成精准化、运维响应预测化五个维度提出系统性优化方案。方案实施后，通过对比关键效能指标（如需求变更响应时长、缺陷密度、部署频率、平均修复时间等）的前后变化，实证验证了AI技术在缩短研发周期、降低缺陷率、提升自动化测试覆盖率与增强团队协作效率方面的显著成效。研究进一步指出，AI工具的有效落地需与组织文化、流程机制和度量体系协同演进，强调“人机协同、度量驱动、治理闭环”的实施路径。 本论文的创造性成果在于：第一，构建了面向中型软件企业的AI研发管理优化实施框架，填补了现有研究在规模化实证与行业适配方面的不足；第二，提出并验证了“体验—过程—结果”三维度量体系在AI赋能场景下的适用性，为效能评估提供可量化依据；第三，揭示了生成式AI在代码生成与测试场景中“辅助而非替代”的核心定位，强调人机协同机制与质量兜底策略的重要性。研究成果不仅为L公司提供了切实可行的改进路径，也为同类型企业智能化转型提供了可复制、可推广的实践范式。 关键词：人工智能；软件研发管理；生成式AI；研发效能；人机协同
英文摘要：	With the rapid advancement of artificial intelligence (AI) technology, its deep integration into the entire software development lifecycle is fundamentally transforming software engineering management models. This thesis focuses on optimizing AI-driven software development process management, using a mid-sized industrial internet company, L Company, as a case study. The objective is to construct an applicable, implementable, and measurable AI-enhanced R&D management optimization plan that boosts R&D efficiency, ensures delivery quality, and controls project costs. Grounded in both theoretical and practical perspectives, the research systematically reviews the latest advancements in AI for Software Engineering (AI4SE), leveraging real R&D data and management status from L Company in the first half of 2025 to identify bottlenecks and quality risks in demand management, task allocation, code review, test automation, and operations. The methodology combines literature review, survey questionnaires, in-depth interviews, and statistical analysis. Initially, it constructs a theoretical framework for an "AI-driven software development process optimization model". Subsequently, addressing specific pain points at L Company, systematic optimization strategies are proposed across five dimensions: intelligent demand analysis, automated design assistance, collaborative development coding, precise test generation, and predictive operations response. Post-implementation, comparative analysis of key performance indicators (such as demand change response time, defect density, deployment frequency, mean time to repair, etc.) demonstrates the significant effectiveness of AI technology in shortening development cycles, reducing defect rates, enhancing automated test coverage, and improving team collaboration efficiency. The study further emphasizes that effective AI tool implementation requires co-evolution with organizational culture, process mechanisms, and measurement systems, highlighting the pathway of "human-machine synergy, metrics-driven, governance closed-loop". The innovative contributions of this thesis include: Firstly, constructing an AI R&D management optimization implementation framework tailored for mid-sized software enterprises, filling gaps in existing research regarding scalable empirical studies and industry adaptation. Secondly, proposing and validating the applicability of a "experience-process-outcome" three-dimensional measurement system within AI-enabled scenarios, providing quantifiable bases for efficacy evaluation. Thirdly, uncovering the core role of generative AI in code generation and testing as "assistance rather than replacement", underscoring the importance of human-machine collaboration mechanisms and quality assurance strategies. These findings not only offer a viable improvement path for L Company but also provide replicable and scalable practices for similar enterprises undergoing intelligent transformation. Keyword：Artificial Intelligence；Software Development Management；Generative AI；R&D Efficiency；Human-Machine Collaboration
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