Software Engineering Principles and Practices

Articles covering the principles, practices, and methodologies of professional software development – from clean code and architecture to testing, design patterns, and modern development processes.

Practical insights for developers at all levels who want to build maintainable, scalable, and high-quality software.

Real Professional Software Engineering in the AI Era

Throughout this series, we’ve established that AI-generated code without understanding creates productivity illusions that collapse in production (Part 1), and that the feedback loop between code and reality—compilation, testing, profiling, production—sharpens thinking in ways AI can’t replicate (Part 2). Now we confront the practical question: What defines professional software engineering when code generation becomes trivial? This final part examines the irreplaceable skillset: understanding execution characteristics (recognizing allocation patterns that cause GC pressure before deployment), asking questions AI can’t formulate (What’s the failure mode when this service is unavailable?), recognizing when plausible AI solutions diverge from correct ones, debugging production failures AI has no execution model to reason about, and evaluating maintainability for code that becomes tomorrow’s burden. We explore why prompt engineering optimizes for speed while architecture optimizes for survival, why “AI productivity” often means faster technical debt accumulation, and why the economic reality favors organizations that measure system reliability over lines of code generated. The feedback loop can’t be automated because closing it requires learning from production failures and applying that knowledge to prevent future ones—the irreplaceable discipline that defines real professionals in 2026 and beyond.

The Feedback Loop That AI Can't Replace

In the first part of this series, we established that AI-generated code without understanding creates an illusion of productivity that collapses under production load. The differentiator isn’t typing speed—it’s the feedback loop where code meets reality and exposes incomplete thinking. But what exactly is this feedback loop, and why can’t AI replicate it? Modern compilers validate logical consistency, catching gaps pure thought leaves unresolved. Profilers expose the 75x performance difference between “seems reasonable” and “actually performs.” Production environments reveal every assumption abstract thinking deferred—scale, concurrency, failure modes. This article explores the mechanisms that transform vague reasoning into concrete understanding: compilation validates logic instantly, testing catches behavioral mismatches, profiling measures what abstract analysis guesses, and production exposes the cost of every deferred decision. Real professionals don’t just write code—they master the iterative discipline of watching it fail, understanding why, and refining their thinking. AI participates in parts of this loop, but it can’t close it. That’s where professionals remain irreplaceable.

Kehrwoche: What Swabian Cleaning Teaches About Technical Debt

Kehrwoche—a Swabian cleaning tradition—is scarier than breaking the build on Friday afternoon. At least the build doesn’t remember next Tuesday. Mrs. Schmid from the second floor does, and she remembers well. What does a weekly cleaning schedule in southern Germany have to do with technical debt? More than most software teams want to admit.

Format Buffet Nobody Ordered

Developers wanted one format. We got twenty. CSV mangles data, XML drowns in tags, JSON forbids comments, YAML punishes spaces. TOML tried fixing it. TAML went minimal. TOON optimized for AI. CCL brought category theory. Result? Five formats per project, three parsers, and debugging why NO became false. AI can’t save us either. Welcome to format hell.

Why Real Professionals Will Never Be Replaced by AI

The elephant everyone ignores: AI can generate code faster than you can type. GitHub Copilot autocompletes entire functions. ChatGPT builds APIs from prompts. Typing is dead. So why will real professionals never be replaced? Because “vibe coding”—describe what you want, ship what AI generates—is a productivity illusion that collapses spectacularly in production. When code generation becomes trivial, understanding what that code costs, where it fails, why it breaks under load becomes everything. AI generates syntax. Professionals understand execution, failure modes, operational cost, and production consequences. The differentiator isn’t typing speed—it’s mastering the feedback loop: write code, watch it fail, understand why, refine thinking. This discipline can’t be automated. Prompt engineers generate code. Real professionals ensure it survives contact with reality.

Most Software Teams Are Lying to Themselves—2026 Needs to Be Different

Happy New Year 2026! 🎉

Fix one piece of technical debt this week—not next quarter. .NET 10, analyzers, and tests are ready; discipline is the only missing part.

2025 in Review: The Year .NET Stopped Lying to Itself

Forget the hype—2025 was when .NET tooling finally stopped pretending complexity doesn’t exist Three tools won by being honest: Aspire exposed topology, TUnit killed flaky tests, Testcontainers made infrastructure real

Stop Typing: The .NET CLI Tab Completion You've Been Missing

One command to transform your .NET CLI workflow—tab completion so responsive you’ll wonder how you survived without it Finally, a productivity boost that’s actually worth your time

.NET Job Scheduling — Choosing the Right Framework

Synthesizing the series into actionable guidance with feature comparisons, suitability ratings, and decision frameworks. Select the scheduler that matches your operational model, infrastructure constraints, and team priorities.

.NET Job Scheduling — TickerQ and Modern Architecture

TickerQ represents the next generation of .NET schedulers with compile-time validation, reflection-free execution, and SignalR-powered monitoring. Understand when modern architecture patterns and performance optimizations justify adopting newer frameworks over established alternatives.

Power of Ten Rules: More Relevant Than Ever for .NET

Gerard Holzmann’s Power of Ten rules prevented spacecraft failures and exposed Toyota’s fatal throttle bugs. Four rules transfer directly to C# with superior enforcement. Three become irrelevant thanks to the managed runtime.

The verdict: These principles aren’t just valid. They’re finally enforceable without heroic manual effort.

.NET Job Scheduling — NCronJob and Native Minimalism

NCronJob leverages IHostedService for lightweight scheduling with zero external dependencies. Understand when minimal infrastructure and native ASP.NET Core integration outweigh advanced features for cloud-native architectures.