A Guide for Software Developers on Generative AI

Automate tedious activities and speed up your coding process with generative AI tools like GitHub Copilot. How? Read on.

With technologies like GitHub Copilot, ChatGPT, and DALL-E becoming more and more popular every day, the era of generative AI has arrived. And while some individuals are still asking, “What is generative AI?” others are asking, “How will this affect my job?” and “How do I get started with it?” Of course, there are individuals who have already joined the bandwagon in full. At Infragistics, we are now running a number of studies to explore fresh capabilities from ChatGPT and OpenAI.

Generative AI can initially appear to be a monster. There are numerous features to access and ways to use it, but you are not required to use them all at once. Here is a starting point for software developers interested in generative AI.

Microsoft also recently released a preview of GitHub Copilot Chat, which is more of a ChatGPT-like interface that lets developers ask questions about code, convert code from one language to another, explain code and more.

With GitHub Copilot, you have multiple options to accelerate your development:

No matter what kind of applications you are developing, you may find a Copilot-enabled feature that will make coding simpler and faster while also boosting the productivity of your software development team.

Developers may speed up a variety of development processes with GitHub Copilot, which will reduce the amount of time they need to spend on repetitive operations. Developers that use an IDE such as Visual Studio, Visual Studio Code, Neovim, or JetBrains can currently test Copilot there. You get instant autocomplete style suggestions for each part of your code you type when GitHub Copilot is turned on. Additionally, you may ask GitHub Copilot for assistance with anything from grammar to authoring full code blocks or pages by utilizing the comment prompt (//).

ChatGPT is a great tool for the same purpose for non-Visual Studio developers. The sole distinction between the two is that ChatGPT is an interactive conversation experience, whereas GitHub Copilot works in context with your code as you type it. This means you could either ask ChatGPT to develop full functions for you or ask it questions about your code for testing or potential security vulnerabilities.

You can instruct ChatGPT to construct a create, read, update, delete (CRUD) web API in Java or Python after providing it with information about your data model. It will accomplish this in less than a minute, but depending on the complexity of your code, it can take you many hours. In this context, generative AI excels and developer productivity can soar.

GENERATION AND COMPLETION OF CODE

Software may automatically generate or suggest code fragments as a developer types. Errors can be reduced and the development process can be accelerated significantly as a result. According to Microsoft, GitHub Copilot generates 46% of a developer’s code, which is a sizeable percentage that will only rise with greater use and more advanced AI models.

Ask GitHub Copilot questions by prefixing your question with //

PROGRAM TESTING

An important part of modern software development, automated testing, can be made more effective with the help of AI. AI has the capacity to design tests, identify when a system is operating incorrectly, and provide specific feedback on the kinds of errors that may have taken place.

OPTIMIZATION OF CODE

To improve the code, AI can suggest adjustments or carry them out automatically. This can entail anything from small performance-boosting changes to major ones that simplify the system’s architecture or just reduce the amount of code needed.

The more monotonous operations that developers can automate, the more time they have to work on challenging and innovative projects. The projects that first inspired developers to become developers can be revisited, with imagination serving as the guide. In fact, according to Github Copilot, 74% of developers who used the tool said it helped them feel less annoyed while they were coding and allowed them to concentrate on more fulfilling work.

For these more intricate and imaginative applications, developers can use some of the generative AI capabilities listed below:

OPTIMIZATIONS FOR DATABASES

AI technologies are capable of building data schemas, making improvements to them, and producing data based on schema knowledge.

PREDICTIVE OR ADVANCED ANALYTICS

Analytics inquiries concerning your data can be promptly answered by AI solutions like ChatGPT. It can, for instance, uncover outliers, correlations, regressions, future forecasts, and other profound data-related insights if you give it a data collection. The model of open tools like ChatGPT will be informed by anything you upload to them, so proceed with caution. As you experiment, keep your information private.

CREATION OF IMAGES

Developers who require graphics generated for projects can quickly receive them with the help of AI technologies, however some are better than others. creating a website to sell rare surfboards? You can obtain excellent digital photos for use in your applications with the correct suggestions.

DESIGN FOR UX/UI

By suggesting or developing user interface designs, anticipating how users will interact with the design, and offering modifications based on user feedback and usage statistics, AI can assist in the UX design process. Despite the fact that this is a very sophisticated assumption of AI, the more data you provide the model with or the better the prompt, the more beneficial the result will be.

While generative AI is a productivity game changer, there are several limitations that every developer needs to be aware of. The model is learning from the millions of GitHub repositories and other public code repositories and sources, and the code created is based on what the model is aware of.

Copilot might not be able to provide useful code suggestions if there isn’t a sizable body of publicly accessible code using a library. Along with other well-known languages with billions of lines of code that are publicly accessible, such as JavaScript, TypeScript, C#, and Go, Python, the most used language in the world, has a very high (40 percent) acceptable code rate utilizing Copilot. If you are utilizing something more recent that does not have a substantial body of work available to the models, your success rate could not be as great.

It’s important to handle AI-generated content the same way you’d treat code from any publicly available source that you didn’t write yourself while using any AI tool. This entails exhaustively testing for performance and functionality as well as checking for security flaws and maliciously created code.

Related posts:

Tepat saat sang fajar mulai memancarkan cahayanya. Daren terbangun saat merasakan getaran yang berasal dari alarm di ponselnya berbunyi. Dengan sedikit mengerang dia berusaha duduk dan mencerna…

The mission of growing your bodily fitness is tough and time-consuming. In addition to regular paintings, own family, and social obligations, seeking to suit right into a well being framework can…

Why does marriage need to be a life-long commitment? The idea of temporary marriage isn't as new or unusual as some may think.