This study investigates the impact of integrating DevSecOps and Generative Artificial Intelligence (GAI) on software delivery performance within technology firms. Utilizing a qualitative research methodology, the research involved semi-structured interviews with industry practitioners and analysis of case studies from organizations that have successfully implemented these methodologies. The findings reveal significant enhancements in research and development (R&D) efficiency, improved source code management, and heightened software quality and security. The integration of GAI facilitated automation of coding tasks and predictive analytics, while DevSecOps ensured that security measures were embedded throughout the development lifecycle. Despite the promising results, the study identifies gaps related to the generalizability of the findings due to the limited sample size and the qualitative nature of the research. This paper contributes valuable insights into the practical implementation of DevSecOps and GAI, highlighting their potential to transform software delivery processes in technology firms. Future research directions include quantitative assessments of the impact on specific business outcomes and comparative studies across different industries.

翻译：本研究探讨了在科技企业中整合DevSecOps与生成式人工智能（GAI）对软件交付性能的影响。通过采用定性研究方法，本研究对行业从业者进行了半结构化访谈，并分析了已成功实施这些方法的组织的案例研究。研究结果表明，该方法显著提升了研发（R&D）效率，改善了源代码管理，并提高了软件质量与安全性。GAI的集成促进了编码任务与预测分析的自动化，而DevSecOps则确保了安全措施贯穿整个开发生命周期。尽管结果积极，但本研究也指出了由于样本量有限及研究的定性性质所导致的研究结论普适性方面的局限。本文为DevSecOps与GAI的实际应用提供了有价值的见解，强调了它们在变革科技企业软件交付流程方面的潜力。未来的研究方向包括对特定业务成果影响的定量评估，以及跨不同行业的比较研究。