The AI-Powered Evolution of Technical Debt Management
A senior software developer John, sits at his desk, engrossed in his dual monitor setup. His fingers dance across the keyboard, a symphony of code creation. Empty coffee mugs are evidence of a long day’s work.
John: (mumbling to himself) Almost there… Just a few more lines and this feature will be ready for testing.
Suddenly, a notification pops up on his screen, a SLACK message from his project lead. It’s marked as urgent.
Alice: (text on screen) John, we have a critical issue on the legacy system. Need you to take a look ASAP.
John sighs, reluctantly pushing his current work aside. He opens the legacy codebase, a tangled mess of outdated programming practices.
John : (under his breath) Not this again…
He quickly scans through the code, trying to locate the problem area. The code is poorly documented, making it a challenge to understand the logic.
John: (frustrated) Who wrote this spaghetti code?
Time is of the essence. He doesn’t have the luxury of refactoring the code properly. He decides to apply a quick fix, just enough to get the system running again.
John: (typing furiously) This should do the trick… for now.
He commits the changes and pushes them to production. The system is back online, but John knows he’s just kicked the can down the road.
John: (to himself) Another band-aid solution. This technical debt is getting out of hand.
He sends a message to Alice, informing her that the issue is resolved, but that the code needs a proper overhaul in the future.
Alice: (text on screen) Thanks, John. You’re a lifesaver. We’ll schedule a refactor for next sprint.
John knows this is a promise that rarely gets fulfilled. He leans back in his chair, feeling a mix of accomplishment and frustration.
John: (sighs) Another day, another technical debt accrued.
If this story is all too familiar, keep reading!!
What is Technical Debt?
Technical debt, a term coined by Ward Cunningham, is a concept in software development that reflects the implied cost of additional rework caused by choosing a quick and easy solution now instead of using a better approach that would take longer.
It is referred to as a catchall that covers everything from bugs to legacy code, to missing documentation.
“Technical debt is the cost of technical decisions that are made for the immediacy, simplicity, or [budget] that, while easy today, will slow you down or increase your operational costs/risks [over time]. Most often it’s related to technical products, but can be found in most business processes and use cases. Many times this technical debt can turn into ‘human spackle,’ where knowledge workers do repetitive tasks that could be automated.” –Justin Brodley, VP cloud operations & engineering at Ellie Mae and co-host of The Cloud Pod
Technical Debt is not limited to code alone. In 2014, a group of academics developed a taxonomy that comprises 13 distinct types of technical debt:
Architecture debt
Build debt
Code debt
Defect debt
Design debt
Documentation debt
Infrastructure debt
People debt
Process debt
Requirement debt
Service debt
Test automation debt
Test debt
Why bother about Technical Debt?
In a McKinsey survey,CIOs reported that 10 to 20 percent of the technology budget dedicated to new products is diverted to resolving issues related to tech debt. More troubling still, CIOs estimated that tech debt amounts to 20 to 40 percent of the value of their entire technology estate before depreciation. For larger organizations, this translates into hundreds of millions of dollars of unpaid debt. And things are not improving: 60 percent of the CIOs surveyed felt their organization’s tech debt had risen perceptibly over the past three years.
Technical debt, accumulated from suboptimal code and design, severely impacts businesses. It leads to frustrated customers, increased support costs, and lost revenue. Developers are forced to fix old problems instead of innovating, hindering a company’s ability to adapt and compete. Over time, technical debt becomes increasingly expensive, consuming resources meant for growth. This results in lower customer retention, less effective marketing, and a damaged brand reputation.
Traditional Methods for tackling Technical Debt
- Identify and address potential technical debt early in development through regular code reviews.
- Catch issues (via testing) before integrating change with the rest of the software.
- Refactor and restructure legacy software without changing external interfaces.
- Document to track and manage the impact of debt.
As seen above, traditional methods of managing technical debt are manual, relying on other team members to identify and catch potential issues in the code. The manual process is inherently error-prone and time-consuming, impacting the delivery schedule. Development teams are unable to keep up with the pace of adding new features vs going back to fixing code.
Enter the Age of Generative AI
Generative AI presents a paradigm shift in code development and management by leveraging machine learning algorithms to automate various aspects of technical debt management, offering faster, less error-prone and automated solutions.
Gemini for Google Cloud Code Assist, which is a product in the Gemini for Google Cloud portfolio, offers AI-powered assistance to help your development team build, deploy, guide and operate applications throughout the software development lifecycle thereby automating code generation and accelerate development timelines and boost developer productivity.
Any software system has a certain amount of essential complexity required to do its job. Lets look at the Technical Debt Quadrant proposed by Micheal Fowler. In every quadrant AI assistants can take up multiple roles from speeding up development to providing guidance, coaching and facilitate implementation best practices.
Boost Developer Efficiency with Gemini Code Assist
Expedite coding with AI code completion, generation, and chat
Application developers can use Gemini Code Assist to auto-complete code inline while coding in IDEs, or generate code blocks using natural language comments. They can also chat with Gemini Code Assist for any code-related questions in IDE
Use smart actions to further expedite development process
Developers can use Gemini Code Assist’s smart actions to automate frequent developer inner loop tasks such as test generation and code explanation. These prebuilt, one-click shortcuts help expedite the development process even further.
Uplevel coding and technical skills
Whether you are looking for answers on how to write specific queries or scripts, for guidance on the best tools or libraries to solve your problems, or searching for coding best practices, you can seek expert-level advice from Gemini Code Assist by chatting with it in natural language right in the IDE, minimizing context-switching.
Managing Technical Debt with the Power of Generative AI
Explaining code with AI assistance helps developers perform effective impact analysis thereby having a prudent view of how new code changes impact the overall functionality. Automated test case generation helps refactor legacy code using TDD principles.
Translating code from one language to another are methods that are particularly useful in modernization projects.
AI Assisted code documentation , code optimization suggestions can significantly increase code quality and maintainability.
AI Assisted log summarization using natural language prompts results in effective debugging as well as uncover potential failure scenarios.
AI Assisted code reviews can provide context aware feedback and detailed recommendations
Value of AI Code Assistants
Summary
Technical debt poses a significant challenge for businesses, often leading to increased costs, inefficiencies, and limitations in innovation. However, by acknowledging the issue and leveraging the power of intelligent AI assistants, organizations can unlock new levels of efficiency, scalability, and confidence in their digital systems.
The future of programming is not a battle between humans and AI; but rather more of a collaboration. AI’s ability to write code means a shift in developers focus from mundane tasks to more strategic ones. Developers will increasingly become orchestrators of AI-driven workflows, guiding the AI systems, refining their outputs, and integrating them seamlessly into the development process.
