Why You Should Read This Book

We can never reason efficiently about a complex system based on its code alone. In doing so we miss out on long-term trends and social data that are often more important than any property of the code itself. This means we need to understand how we—as an organization—interact with the code we build.

This book shows you how as you learn to do the following:

• Use data to prioritize technical debt and ensure your suggested improvements pay off.

• Identify communication and team-coordination bottlenecks in code.

• Use behavioral code analysis to ensure your architecture supports your organization.

• Supervise the technical sprawl and detect hidden dependencies in a microservice architecture.

• Detect code quality problems before they become maintenance issues.

• Drive refactorings guided by data from how your system evolves.

• Bridge the gap between developers and business-oriented people by highlighting the cost of technical debt and visualizing the effects of refactorings.

If all this sounds magical, I assure you it’s not. Rather than magic—which is usually a dead end for software—this book relies on data science and human psychology. Since we’re part of an opinionated industry, it’s hard to know up front what works and what doesn’t. So this book makes sure to include references to published research so that we know the techniques are effective before attempting them on our own systems.

We also make sure to discuss the limitations of the techniques, and suggest alternative approaches when applicable. As noted computer scientist Fred Brooks pointed out, there’s no silver bullet. (See No Silver Bullet—Essence and Accident in Software Engineering [Bro86].) Instead, view this book as a way of building a set of skills to complement your existing expertise and make decisions guided by data. The reward is a new perspective on software development that will change how you work with legacy systems.