This study explores the effectiveness and efficiency of the popular OpenAI model ChatGPT, powered by GPT-3.5 and GPT-4, in programming tasks to understand its impact on programming and potentially software development. To measure the performance of these models, a quantitative approach was employed using the Mostly Basic Python Problems (MBPP) dataset. In addition to the direct assessment of GPT-3.5 and GPT-4, a comparative analysis involving other popular large language models in the AI landscape, notably Google’s Bard and Anthropic’s Claude, was conducted to measure and compare their proficiency in the same tasks. The results highlight the strengths of ChatGPT models in programming tasks, offering valuable insights for the AI community, specifically for developers and researchers. As the popularity of artificial intelligence increases, this study serves as an early look into the field of AI-assisted programming.

Recent advances in DevOps have dramatically reshaped software development and operations by emphasizing automation, continuous integration/delivery, and rapid feedback. However, organizations still struggle to achieve predictable improvements despite widespread adoption. In this study, we propose an “IDEAL-Enhanced DevOps” framework that integrates the five-phase IDEAL model—Initiate, Diagnose, Establish, Act, and Learn—into a DevOps transformation process. The proposed method lays out a structured approach to applying incremental improvements throughout the software delivery process. By a review of literature, in-depth analysis of case studies, and dis-semination of a questionnaire to practitioners in the field, this research explains how the IDEAL stages can be mapped to key processes in DevOps, address automation and scalability challenges, and facilitate a learning-centered, cooperative culture. The results show that a well-defined process-improvement approach can effectively reduce error incidence, enhance usability of tools, and significantly shorten time to get products to market. Our analysis shows that coupling IDEAL with DevOps not only clarifies responsibilities and organizational roles, but also lays a foundation for more resilient, high-quality, and adaptable software engineering methods.