Developers, architects, and application teams are constantly chasing technical debt. For better or worse, it’s a nagging problem that too often gets kicked down the road until it’s too late and application development slows down, new features slip, test cycles increase, and costs ramp up. In the most public situations, the applications tip over completely — like we’ve seen most recently at Southwest Airlines, Twitter, FAA, and others which never get publicized — but you know who you are. Technical debt takes on various forms from source code smells to security risks to the more serious issue of architectural technical debt. Great tools exist to scan for source code quality and security, but tracking, baselining, and detecting architectural drift has been hard due to the lack of observability, tooling, and best practices.

What exactly is architectural technical debt and why should I care? If you are an architect or developer responsible for maintaining and extending an older Java or .NET monolith, you probably are already deeply familiar with the problem. A monolithic application is actually defined by its architectural (monolithic) pattern which carries with it dense dependencies, long dependency chains, and in essence a big ball of mud that is opaque for any architect trying to understand and track. This is the essence of architectural technical debt: the class entanglements, deep dependencies, dead-code, long dependency chains, dense topologies, and lack of common code libraries that plague monoliths, older applications, and more recently even microservices that have begun to resemble monoliths themselves.

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