How Can AI Help You Meet Deadlines? The Answer Lies in Tackling Technical Debt
Do you find it really frustrating when you've worked so hard on a project, even sacrificing weekends, and still can't meet your deadline? Your project might just be suffering from improper or ineffective technical debt management.
But don't worry, there are ways to tackle technical debt and stay on track.
In this article, we'll explore some AI tools and techniques to help you manage technical debts, such as test debt, bug fixing debt, refactoring debt, and documentation debt more effectively. Implementing these techniques would help you save time and meet your deadlines.
But before diving in, let's take a moment to understand why technical debt isn't always bad.
Technical Debt Isn't Always Bad
Technical debt is often the cause of project delays, however, it can also be a strategic choice to meet project timelines.
Understanding the two types of technical debt is key: planned and unplanned. Planned debt is intentional and comes with a repayment plan, while unplanned debt stems from poor practices or resource constraints.
Sometimes, in certain market conditions, you may have to take on technical debt to meet deadlines, fix critical bugs, or deliver specific features quickly.
For example, suppose you are working on a feature that needs to be ready by the holiday season. If you miss this deadline, you will have to wait until the following year for another opportunity.
It makes perfect sense to take shortcuts or incur planned technical debt to meet your deadlines in this situation. However, if not planned properly, technical debt can cause significant issues in the future.
There are several ways you can incur technical debt in your project. Let's explore some of them.
Common Causes of Technical Debt
Technical debts can take various forms, some of which include
- Insufficient or ineffective testing
- Software architecture debt
- Non-adherence to coding standards
- Inadequate infrastructures
- Lack of proper documentation
Initially, they may not appear significant, but over time, they accumulate and become problematic.
For instance, ignoring testing debt can have devastating effects on a project, especially when combined with infrequent releases, increasing the likelihood of overwhelming amounts of bugs. This could lead to an endless cycle of bug-fixing, ultimately slowing down your project progress.
Thankfully, with the emergence of AI tools, managing technical debt has become less time-consuming, enabling software development teams to meet their deadlines while maintaining the overall quality of the product.
How to Manage Technical Debt with AI: Tools & Techniques
Managing Test Debt
Repaying testing debt can be tedious, but tools like Github Copilot and ChatGPT can save you 100+ hours of testing time.
For instance, Instead of spending a lot of mental energy brainstorming test cases, ChatGPT can generate ideas for test cases specifically tailored to the feature you're working on. You can then choose the ones that are most relevant to your needs.
Combining this with code generation and suggestion features of code assistants like Copilot can make writing these tests even quicker, saving you tons of time and meeting your deadlines.
Managing Bug Fixing and Code Refactoring Debt
According to a report, developers spend an average of 17.3 hours per week debugging and refactoring code. That's almost half of a full workweek dedicated to fixing bugs and improving code. Imagine if you could reclaim some of that time, you might even conclude your project ahead of schedule.
Although still in its experimental stages, AI tools like Github Copilot Labs can help you fix bugs and refactor your code much faster. With Copilot Labs, you can easily identify and fix bugs with the click of a button. Not just that, you can refactor your code, making it much easier to read and maintain.
Let's say you have a lengthy function that you think would be better split into smaller functions. With Copilot Labs installed in your VS Code, simply highlight the code block, and with the click of a button, it will break it down into smaller, more readable functions.
The less time you spend fixing bugs and refactoring code, the more likely you will meet your deadlines.
Code Analysis and Review
If you’re an engineering manager or tech lead, you're probably familiar with the frustration of being pulled away from important tasks to review code.
With AI-powered code quality tools such as SonarQube, it might not be as frustrating anymore. SonarQube uses machine learning to analyze your code, checks for code quality, detects bugs and vulnerabilities, and identifies areas that may not have been properly tested.
In other words, it does all the things you would normally do when reviewing code.
You can integrate it into your CI workflow, meaning you can catch potential errors before you even lay eyes on the code. Imagine the time saved! You'll have more time for other tasks that add value to your product and ensure that you meet project deadlines with high-quality software.
Code Generation and Documentation
Writing self-explanatory code is a good practice, but there are times when code blocks or functions are too complex to understand without comments. Adding comments can be a lifesaver when revisiting the code months later or when someone else needs to understand your work.
Thankfully, tools like Github Copilot and Open AI’s chatGPT can generate helpful comments for your code. By inputting your code snippets into these tools, you can quickly generate comments that are both accurate and easy to understand, eliminating the mental strain of writing them from scratch.
In addition to documenting your code, you may also need to create test documents that define the scope of what needs to be tested. This helps ensure that you're testing the most critical parts of your project and meeting your deadlines effectively.
With text generation AI tools like chatGPT, you can easily create these test documents, saving you time and allowing you to focus on the code that results in the functionalities your end users want.
Challenges and Limitations
AI features may seem incredible, but they are not without their challenges. If not handled correctly, they could actually hinder project progress and cause delays. Let's take a look at some challenges and limitations you might encounter when using AI to manage technical debt.
Not a Plug-and-Play Solution
“AI will take all developer jobs” — someone who's never had to fix a production bug at 3 a.m.
I just raved about how AI can manage technical debt and eliminate the delay of project deadlines, so why am I saying the complete opposite?
Many people have this idea that AI can handle everything for developers. They think that they can tell the AI what to do, sit back, and watch it build a high-performing, scalable application. But that’s not realistic.
Yes, AI can speed up the process, but it's not a cure-all solution. To effectively manage technical debt, you still need human supervision or assessment to ensure that it is applied effectively to your project.
Integration and Adaptation in a Team
Not everyone knows how to use AI tools effectively. Some team members might struggle to express their needs and instructions clearly, which could lead to spending more time trying to get results from AI tools than they would by doing the task themselves.
Additionally, getting everyone on a team to agree on the usefulness of AI tools can be a challenge, as each team member might have different thoughts and biases towards AI.
To overcome this, good monitoring of AI tool usage and demonstrations of its benefits may be necessary. By showing how these tools can help each member of the team or the entire team achieve their goals, team members may become more willing to use them effectively.
Overreliance on AI
We can all relate to the reliance on tools, whether it's a familiar code editor or the convenience of autocompletion. Imagine having to code with Vim for a day – it probably wouldn't be your best day at work.
Don’t worry, relying on autocompletion doesn’t remove your super dev status.
When it comes to AI tools though, it's important to avoid relying on them too heavily. Doing so can lead to laziness or over-trusting, which might cause you to skip necessary scrutiny and validation of code responses.
Just because it worked perfectly yesterday doesn’t necessarily mean it will do the same today. If you’re not careful, it could lead to more technical debt down the line, invalidating the purpose of using AI in the first place.
The copyright and privacy rules for code produced by these AI tools are still ambiguous. This means that if you're developing a unique or proprietary feature, you should consider the potential legal consequences for your product or business before using them.
There Will Be More to Come
Expect to see an increase in AI tools in the coming years. As AI technology continues to advance, these tools will become more prevalent, creating new ways to manage technical debt and increase productivity.
As they do, make sure to explore and experiment to find the best fit for your needs. But more importantly, stay vigilant so that the tools you choose don't create more problems than they solve.