Duct-taping Software – What is Tech Debt in Software Product Development and How to Manage it? [An Infographic]

We have something very common to explain you but in an unusual way 😊

1) What is tech debt?

Tech debt is when you apply some shortcuts in your software product development lifecycle to meet a deadline more quickly, but later find serious flaws in the software and need to duct-tape it.

tech debt

2) How to measure tech debt?

The simple way to measure tech debt is to answer a question – is your product being supported by mere one or two contributors to successful product development? If yes, high tech debt is found!

For your context, a successful product is being contributed by code quality, scalable & modular architecture, intuitive UX, seamless integration, low bug density, minimum maintenance efforts, good release stability, and dynamic performance.

tech debt

3. How to manage tech debt?

No, that’s not how you manage tech debt. (Don’t worry, we have a whole infographic for you.) For now, let us tell you one thing – to manage tech debt, you must first find the ‘inflection point’ of your software.

Because, sometimes, you become a victim of ‘opportunity cost’ and invest efforts in supporting your software even if it can thrive without that support. Just like this Leaning Tower of Pisa. It does not really require any support to stand epic!

tech debt

How to manage tech debt?: An infographic

How would tech debt unfold in 2024 as developers embrace AI for code generation, testing, and beyond?

An AI answer:

“The future of tech debt, as developers embrace AI for code generation, testing, and beyond, is an interesting and evolving topic.

AI technologies can certainly offer benefits and efficiencies in software development.

AI-powered code generation tools have the potential to significantly speed up the development process by automatically generating code snippets, templates, or even entire functions. These tools can help reduce manual effort and increase productivity.

Similarly, AI can play a role in automated testing, helping developers create test cases, analyze code coverage, or even perform automated bug detection. This can enhance the efficiency and effectiveness of testing processes.”

A human answer:

“Indeed, AI-powered code generation tools have the potential.

However, if not used judiciously, they can also lead to the accumulation of low-quality or poorly maintainable code. This can result in increased tech debt if developers rely too heavily on AI-generated code without thoroughly understanding or reviewing it.

Similarly, relying solely on AI for testing without human oversight can lead to blind spots or false positives/negatives, potentially resulting in undetected issues that contribute to tech debt.”