Why You Should Use AI to Improve Your Productivity As a Software Developer

ChatGPT isn’t the first AI-assisted coding solution for programmers. Tools like GitHub CoPilot and Tabnine have existed since 2018.

But what makes ChatGPT different is that it doesn’t just offer guidance or hints; it actually communicates with you, learning from your coding techniques and giving you tailored answers to specific questions.

That marks an interesting shift in the augmented coding movement. AI tools were never good for complex use cases because they didn’t understand the context well enough. With ChatGPT, however, AI is now proactively asking questions and clarifying context so that it can better understand your use case to offer tailored recommendations.

Does that mean AI is going to replace developers altogether? Not at all. But it can be an interesting addition to your programming stack that enables you to code with more efficiency.

In this article on AI-assisted coding, we’ll go over the basics of augmented coding platforms to understand their usefulness and failings. Then, we’ll talk about integrating artificial intelligence into your daily software development workflow. Finally, we’ll offer suggestions for breakthrough platforms that can help you generate code with AI.

Understanding the Basics of AI-Assisted Coding

At its core, AI-assisted coding is about simplifying the process of writing code. It’s similar to how an auto-complete feature works in any text editor; it provides suggestions based on what you type and the context in which you’re typing.

The difference with AI-assisted coding is that these systems are much more powerful. They understand the entire codebase and can offer recommendations on changes or additions that could optimize your code.

They can also provide insight into potential issues before they become bugs, spot patterns in data, and even alert you when your coding style starts to diverge from established best practices. In essence, they act as another pair of eyes, helping you craft better code without sacrificing development speed.

At least, that’s how it works theoretically. In practice, there are still a few hurdles to overcome.

For starters, AI-assisted coding tools must be optimized to your existing coding practices so that they can offer tailored recommendations. That requires lots of upfront training.

Boris Paskalev, CEO of DeepCode, puts it best in an interview with VentureBeat: “Since there are no formal rules for software development, programming is an art that requires a complete understanding of code and a developer’s intentions to produce something that works as expected without bugs.”

Moreover, while they can offer valuable suggestions on how to optimize code, they can’t guarantee results. (OpenAI Codex claims to generate accurate code only 37% of the time.) You still have to vet every suggestion yourself and run quality assurance checks to verify that it works. That can sometimes result in lots of manual work if you’re working on something complicated!

In 2022, a team of researchers released APPS, an open-source benchmark for code generation using NLP. The team tested several types of AI models, including OpenAI’s GPT-2, GPT-3, and an open-source version of GPT-3 called GPT-Neo. They found that most models couldn’t generate code without introducing syntax errors, even when it came to solving the most simple problems. GPT-Neo was the best-performing model, with an accuracy of only 10.15%.

When Should You Use AI to Augment Developer Productivity?

AI-assisted coding platforms aren’t right for every project. They work best when you have an established codebase and are looking for ways to improve it. That’s because they need to understand the context to provide meaningful suggestions.

If you’re building something from scratch, however, then these platforms will be of limited use. In that case, it’s best to work manually or with a template using conventional programming tools.

That said, there are plenty of situations where AI can be a valuable asset for developers: when you’re just trying to refactor existing code or need to automate tedious tasks. In those cases, AI-assisted coding can be a great time-saver.

Integrating Artificial Intelligence Into Your Development Workflow

AI works best when it’s integrated into your existing workflow rather than programming from a blank slate. It also works better the more specific your instructions are on how to generate code. Some of these platforms specialize in a particular programming language, whereas others are meant for more generalized use cases.

Here are a few tips for integrating AI into your workflows for maximum efficiency:

• Start by identifying which areas of your code can be automated. AI-assisted coding platforms don’t work well with overly complex tasks, so focus on simpler processes first.
• Get familiar with the platform you’re using. Different platforms have different capabilities, so make sure you understand what they can and cannot do.
• Provide clear instructions on how the AI should generate code. This will help the system understand your coding practices and offer better recommendations.
• Keep track of changes made by the AI-assisted coding platform to ensure that it’s helping (not hindering) your development process.
• Make sure to check for quality assurance to verify that the generated code works properly before deploying it into production.

5 Breakthrough AI Platforms for Developers

With the field of AI-assisted coding growing rapidly, there are lots of options for developers to choose from. Here are a few platforms that have caught our eye:

OpenAI Codex

OpenAI Codex is an open-source platform developed by OpenAI that specializes in automated code generation and optimization. It uses natural language processing to generate code and offers a range of customization options tailored to your programming language and preferences.

DeepCode

DeepCode is an AI-powered development platform that helps developers find and fix bugs quickly while they’re writing code. It offers recommendations on how to improve existing code as well as refactoring suggestions based on industry best practices.

Google Cloud Code

Google Cloud Code is a comprehensive development tool that includes an AI-assisted programming feature. It provides autocomplete suggestions and code generation, helping developers to quickly and accurately write their code.

Microsoft Code Accelerator

Microsoft Code Accelerator is a platform for AI-assisted development that helps developers create better software faster. It offers code analysis, debugging recommendations, and automated refactoring tools tailored to the language you’re using.

Kite

Kite is an AI-powered coding assistant developed by Google that helps developers with real-time coding assistance while they’re writing their code. It provides auto-completion based on its understanding of your syntax and semantics, as well as feedback on errors in your code.

AI + Time-Tracking: The Ultimate Developer Productivity Toolkit

At 7pace, we’re always looking for new ways to make our development workflow more efficient. That includes experimenting with a wide range of AI-enabled coding platforms, such as the ones you just read about.

For software developers looking to maximize their productivity, we recommend using AI-coding platforms like ChatGPT with a performance optimization tool such as our Timetracker for Azure DevOps and GitHub.

With that, you can measure and quantify the tangible impact that artificial intelligence is having on your productivity. It gives you an uncanny level of visibility and control over their workflows, enabling developers to experiment with different platforms for maximum results. You can optimize workflows, save resources, and deliver projects on time and within budget.