Bugged_But_Haapy

The Future of Software Engineering in the Age of AI Agents The Bugged but Happy The Future of Software Engineering in the Age of AI Agents We’re at a tipping point. From AI-assisted code completion to autonomous agents that can design, test and operate software, the software engineering landscape is changing faster than many teams can reorganize. This article explores what that future looks like — technically, operationally, and ethically — and gives practical guidance for engineering teams that want to thrive. 1. The trajectory so far: assistants → agents The last decade in software engineering has been dominated by tooling that increases developer productivity: integrated development environments, continuous integration, containerization. More recently, we added AI-powered assistants — code completion, linting, and test suggestion. These ass...

Self-Healing Tests and Beyond — Building Resilient Automation with AI Self-Healing Tests and Beyond — Building Resilient Automation with AI How AI can stop your test suite from becoming a maintenance nightmare — practical patterns, research evidence, case studies, and a roadmap for adopting self-healing automation. Abstract Automation promised freedom from repetitive manual checks. Instead many teams got a new job: maintaining brittle test scripts. A small CSS change, renamed API field, or timing difference can turn a green pipeline into a red alert parade. Self-healing tests, powered by AI, offer a different path. They detect when tests break, reason about intent, and adapt — sometimes automatically — so pipelines stay useful rather than noisy. This article explores the idea end-to-end: what self-healing means, how it works, evidence it helps, tool opt...

Visual Testing with AI: Smarter than Pixel Matching Visual Testing with AI: Smarter than Pixel Matching Practical, human-centred guidance on moving from brittle pixel diffs to perception-driven visual testing — with research evidence, real case studies, tool guidance, prompts, and an adoption checklist. Abstract Visual correctness is one of the most under-appreciated dimensions of product quality. Unit tests and integration tests prove that code works; visual tests prove that people can use it. For years teams relied on pixel-by-pixel screenshot diffs to guard the UI. The result was mountains of false positives, developer fatigue, and missed user-impacting issues. Today, perceptual visual testing powered by AI provides a better signal: it understands components, spatial relationships, and usability impact. This article is a practical synthesis ...

AI for Software Architecture & Design Patterns | Smarter System Design with AI Agents AI for Software Architecture & Design Patterns Abstract Software architecture defines the structural and behavioral boundaries of a system. It shapes scalability, maintainability, resilience, and cost over the product lifetime. Recently, AI agents—driven by large language models (LLMs) and agentic toolchains—have begun to assist engineering teams with architecture drafting, pattern detection, and living documentation. This article synthesises empirical evidence, real-world experiments, practical prompts, and governance advice to help teams adopt AI-assisted architecture responsibly. 1. Why Architecture Still Matters Architecture decisions propagate. A single early choice—how respon...

Code reviews and pull requests are the heartbeat of modern software development. They’re where teams enforce standards, debate approaches, and catch mistakes before they slip into production. But anyone who has spent late nights combing through large diffs knows they can also be slow, tedious, and inconsistent. Copilot changed how developers write code. Now, AI agents are beginning to change how we review it. They don’t just autocomplete functions — they scan diffs, highlight risks, suggest tests, and even draft polite review comments. If Copilot was autocomplete on steroids, AI review agents are like having a sharp-eyed teammate always available to sanity-check your code. This piece continues the narrative from Blog 1 (which explored agents moving beyond Copilot in code generation). Here we look at the review side: research, tools, developer experience, risks, and where this is h...

Autonomous Testing with AI Agents: The Future of QA Imagine a release day where QA is not the bottleneck. The build is green, feature flags are set, and the pipeline hums along—because testing isn't waiting on humans to run scripts. Instead, intelligent agents have already learned the app's flows, executed hundreds of scenarios overnight, and surfaced only the high-confidence issues that truly need human judgment. Why testing still feels broken If you've been in software for more than a few sprints, you've seen the cycle: new features land, automated scripts break, and testers rewrite brittle tests. Manual regression becomes a time sink. Releases slip. Stakeholders lose confidence. The labor of maintaining scripted automation often overshadows the work of exploring real product risk. What are autonomous testing agent...