Letting software engineers utilize artificial intelligence code promotes a synergy that profoundly benefits company projects. You combine in-house expertise with AI’s ability to learn, adapt, and make decisions based on data-driven insights.
When AI influences the workflow, developers become more productive. Functional code appears faster, and many responsibilities fall off the task list due to automation. For instance, a team might employ AI to generate code and define database parameters.
This article defines AI code, explores its benefits to developers, and discusses common applications of AI in software development.
What is AI code?
AI code comes from AI or machine learning (ML) models. Developers can use AI to code custom software, automate tasks, generate new code on demand, and more. How your team interacts with the technology depends on what you’re trying to achieve.
Behind many AI coding tools are large language models (LLMs). With the LLM, developers can ask for the code they need in natural language. Or, if they’re programming, AI-generated suggestions and auto-complete increase efficiency while eliminating context switching. There’s no need to move between Google and an integrated development environment (IDE). Research, brainstorming, and coding can all happen in the same environment.
Benefits of using AI in coding
The benefits of using AI for code are so significant that incorporating AI into your development strategy changes how you create software.
Stack Overflow’s 2023 Developer Survey found that 70% of developers use AI or plan to use it soon, while 77% view the technology favorably. Here are a few benefits of coding with AI:
Improved efficiency and productivity
Productivity surges when DevOps and IT teams collaborate with AI. The AI handles low-level tasks, such as debugging, in the background while providing a 24/7 code generator. The developer gains efficiency, and their code output rises.
Within a DevOps framework, AI can perform testing and deployment activities. It can analyze the codebase and further increase efficiency by helping optimize resource allocation. Using AI could ultimately affect your company’s cash flow.
It may take your team a while to adapt, so a trial-and-error period is perfectly normal when first adopting AI.
Reduction in coding errors
One of AI technology’s biggest strengths is its precision. Ask a human being to write a function 100 times, and they’ll likely make a typo or other mistake. Get an LLM to complete the same task, and perfection is a near guarantee.
AI-generated code snippets are more likely to be error-free than those created by humans. When the developer does write code, AI tools can flag mistakes and help with documentation. No matter how long or complex your codebase is, an AI tool can read every line.
Enhanced code quality and consistency
Fewer errors in code equate to higher consistency and quality. Understanding codebase structure helps AI recognize patterns, helping developers adjust their coding style. AI software can spot the discrepancy immediately if someone veers off the path.
DevOps principles focus on consistency because it translates into better software. Everyone knows what to expect, and no one spends hours figuring out someone else’s dense or unusual code.
Speeding up the development process
Harnessing the power of AI speeds up everything. Using AI code lets the team complete projects on shorter timelines without compromising important things.
AI technologies can automate tedious yet critical tasks that bog down your team. For example, AI can instantly adapt code samples to fit the neighboring context.
AI productivity and its time-saving features extend beyond automation. You can set up AI support directly in your IDE and let it speed up code creation. AI assistants read context clues such as function and class names, offering help and automatically completing the code.
Teams that practice continuous integration appreciate how AI can smooth out errors before they reach the rest of the project. Without AI error checks, poor code might survive the integration step and live in the repository.
Common applications of AI in coding
There are many ways to use AI for coding. Each could have merit for your business. For example, you can add AI functionalities to your code or request an in-depth code analysis.
Here are some typical uses of AI in coding:
Automated code generation
Use AI to generate independent or complementary code whenever you program. AI software is still far from glitch-free, so you shouldn’t depend solely on AI for complicated or lengthy code. But the technology is perfect for generating short chunks of code that you can tweak.
You can ask for an entire function, or the AI could fill in the gaps in your current application. To save time at the start, consider requesting boilerplate code.
Code review and bug detection
Developers devote much effort to reviewing their code and combing for bugs, which is necessary to keep the DevOps pipeline flowing seamlessly. AI can step in and make code reviews easier for everyone.
AI can identify errors more quickly and reliably than human developers. In a 2024 study published in SoftwareX, developers using an AI code review plugin detected 28 code smells, while those without AI assistance only found 20.
Predictive analytics for coding
AI knows what you want to code next. AI-driven predictive analytics uses data mining and ML to forecast the future. It builds the analysis on code complexity, developer productivity, and past project outcomes.
AI can finish lines of code when you get stuck, keeping your speed where you want it. It can also recommend areas where you should focus your energy or predict how long it will take to complete specific tasks. Integrating AI with platforms such as Jira deepens its data-gathering skills.
AI-driven code optimization
It’s hard to visualize better code when you work on a years-old codebase. Tiny, unnoticed inefficiencies become part of day-to-day operations. AI can swoop in and discover opportunities for optimization.
AI-driven optimization starts by examining your codebase, highlighting redundancies and areas for improvement. After analysis, AI might suggest installing code patches or new libraries.
How to start with AI coding
You can start working with AI immediately, but the benefits may only appear once you fully understand the tools. Follow these basic steps to integrate AI into your coding workflow:
- Research your toolset. Think about cost, ease of use, purpose, and scalability.
- Prioritize integrations. Jumping from platform to platform increases the risk of errors and wastes time.
- Train the team. Ensure everyone who will be using AI understands the new software.
- Always read AI results. Never push AI code without viewing it.
- Continuously monitor results and make adjustments as needed.
Selecting the right AI coding tools might be the most critical step. They all use the same technology, but they won’t all be the right fit for your business model. To make the right choice, research thoroughly, read reviews, and speak to industry leaders about the AI software they employ.
Look for AI software that offers a free trial period to test your choice before committing.
No matter how well-suited your AI is, it likely won’t come problem-free. There are common challenges that affect developers using AI.
One such challenge is the potential harmful bias in AI algorithms. If you’re using AI to make decisions about people, the code mustn’t discriminate based on factors such as race or gender. Unfortunately, this isn’t automatic. You’ll have to run numerous tests and analyses to ensure the algorithm is bias-free.
Another challenge is that developers may become overly dependent on AI and lose their intuition when finding code errors. The cure is to ask why the code failed. If the developer can explain the mistake, they stay sharp.
Leverage AI code for enhanced Jira workflows
Leveraging AI code empowers your developers, and you can heighten the impact by integrating Jira. Jira workflows describe the paths through the system that bring an issue from first mention to completion. AI enhances whatever workflow you develop, enabling shortcuts such as auto-tagging and automatic issue assignment.
There’s a Jira workflow for every project, including problem workflows, incident workflows, risk management workflows, code review workflows, DevOps workflows, and many more.
AI code simplifies workflows by limiting errors and improving each developer’s productivity. With AI to eliminate clunky code and mistakes, your team can finesse the codebase and architecture until they complete optimization.
See what merging AI and Jira can do for your business today.