Examples of AI in Software Development

As artificial intelligence (AI) has shifted from a movie concept to a real-world phenomenon, it’s become clear that AI will change how we do business. Smart factories, connected shop floors, and supply chain automation are already commonplace, yet manufacturing and shipping are not the only sectors adopting AI and machine learning (ML). AI is also revolutionizing software development in surprising ways. From project planning to quality assurance (QA) testing and beyond, AI-powered software development is not only the future; it’s already here. What are some examples of AI in software development in use today — and what can we expect moving forward?

What is AI?

Before you learn how AI and ML are changing the game in software development, it helps to define what they are. These two terms are often misunderstood — and even feared. For example, some worry that AI-powered automation will replace human workers, but the truth is quite the opposite. Instead of replacing programmers, AI tools allow them to do more — and do it faster and more accurately.

Artificial intelligence is the catch-all term for computer-based systems that perform tasks that usually require human intelligence. The ability to learn, reason, and decide are all hallmarks of AI technologies. AI allows a computer to “sense” its environment, learn from it, and use machine learning algorithms to take action without human intervention.

While AI is an umbrella term, machine learning refers to a specific subset of AI. ML systems use algorithms to "learn" from previous interactions. Using this accumulated knowledge and experience, ML then makes reasoned decisions.

You can think of the spelling and grammar checker in your word-processing app as a basic form of AI. Using a dictionary and the rules of grammar, the checker can analyze your text and point out errors like a human reader could. ML technology lets the checker become smarter over time by learning the context of your writing. For example, slang and catchphrases often don't follow grammar rules. But by learning the context, an ML-powered spell checker would learn not to flag them.

Amazon Alexa is a good example of ML in our everyday lives. Over time, Alexa learns the nuances of how you speak and gets better at responding to your commands.

People are building AI and ML solutions in all industries to automate manual tasks and solve specific challenges. Over time, AI tools acquire more data and thus learn more, proving self-optimizing as smart apps that better understand real-world scenarios and solutions. 

Out of the many components and tasks involved in programming and building apps, that self-optimizing ability makes AI a natural talent. Software development has benefited substantially from AI's many advancements. Here are some ways that AI is optimizing the software development lifecycle.

AI Enables Rapid Development

Testing is a key stage of the software development process, but it can be slow and laborious. Unless an application serves one small function, testing every possible usage scenario for bugs can be difficult — as can finding appropriate human beta testers and having them try the app in real-world situations.

Machine learning is ideal for testing because AI can run many automatic tests and report the results. By doing this task faster and more accurately than human testers, AI saves time and money. It also ensures that the fewest bugs make it into a shipping product, preventing the many problems associated with shipping buggy software.

AI Handles Error Management

Errors are bound to happen when an app is in constant use. Unfortunately, even the best software is prone to this problem, which can result from user error, hardware failures, network interruptions, and more.

In the past, correcting this issue has required development teams, QA analysts, and support staff to manually troubleshoot the root cause of errors. Now, AI is making big improvements to the error management process. Real-time monitoring is one way, but AI can learn from past mistakes through software analytics data. When AI assistants comb through this data, they gain enough knowledge to identify the cause. In many cases, AI can even suggest the fix.

Natural Language Processing Automates Support

Natural language processing (NLP) is a component of AI and ML that allows apps to understand human languages as they are spoken and written.

NLP scans data inputs and looks for patterns. It’s a prime example of AI learning user behavior and understanding how humans communicate. 

You’ve likely had experience with chatbots. On many websites, a box will pop up and ask if you have any questions. Based on the questions you ask, you’ll either get an answer or be directed toward someone who can help.

These bots can learn and provide a human-centric customer support experience using existing customer support data. AI technology is also used in other contexts, like call centers. For example, when a customer calls with a frequently asked question, the AI directs them toward self-service options or determines if they need to talk to a live operator.

As more customer support information enters these systems, data analytics equip the support bots with even more knowledge. 

The Emergence of Generative AI

AI and ML have been a part of the software development space for some time. Now, the field is evolving with generative AI — a technology that can generate unique content, such as text and images. The emergence of the ChatGPT intelligence software has made the concept of generative AI mainstream. As many curious users found out, you can ask ChatGPT to write about any topic. The software quickly returns some impressive results using information publicly available on the web.

Even before ChatGPT and other generative tools like text-to-image converters, developers realized the power of generative AI for creating code and speeding up the development process.

These developments have also highlighted the value of APIs in software development. Developers can rapidly deliver powerful new software using APIs with generative AI software.

The Benefits of APIs and Generative AI

An API (application programming interface) is a programming concept that’s been around since the beginning of software development. APIs provide a method for exchanging data and services between different software sets, enabling a rapid information exchange across platforms.

APIs have been key in transitioning from monolithic software development architecture to microservice architecture. By integrating the services of multiple applications, developers can build new and focused apps in record time. Generative AI fits perfectly into this model.

When developers use APIs for generative AI software tools, they can increase the efficiency and scalability of their apps. This novel approach has proven especially true in Big Data organizations. 

Facilitated through APIs that grant access to the information, AI can scan large datasets and identify the appropriate content for applications, saving hours of development time. 

The Future of AI in Software Development

One of the more exciting trends in AI-assisted software engineering is the concept of deep learning.

Deep learning is a form of machine learning where an algorithm uses vast amounts of data to learn independently. Deep learning uses neural networks that mimic the human brain, making this technology closest to human intelligence. 

Some open-source development tools are already making deep learning technology available to organizations eager to try it out. For example, in the world of proprietary software, deep learning algorithms are teaching self-driving cars how to react to things like changing street lights, obstacles in the road, and other situations that require time and experience for proper decisions.

The Importance of APIs in AI

While generative API is a new mainstream trend, it is more familiar to organizations committed to the microservices architecture. Developers at these companies already know the importance of APIs.

Smart factories, automated supply chains, robotics, and more staples of Industry 4.0 depend on APIs for rapid and efficient data exchange. For example, imagine a robot that pulls inventory from your shelf for shipping. It would have utilized an API to query your inventory system and locate the item.

On an AI-equipped manufacturing floor, sensors check to ensure equipment is in-spec and operating normally. If a problem needs attention, an API would send out a notification.

Much of “the magic” that happens with AI and ML can be attributed to APIs sharing information between devices or systems. This is in line with the concept of the microservice architecture — sharing information as needed and creating new functionality from the data and services of separate systems.

Data sharing highlights the importance of APIs in business and software development. No matter how automated the process is or how much information AI consumes or generates, there must be a way to share the information.

For an organization to manage this vast amount of data, it must have an API management platform. These tools help you centralize the management of all your APIs and perform essential functions like documentation and testing. However, with so many APIs in use, manual management processes are sure to become problematic over time.

DreamFactory: The Comprehensive API Management Platform

It's time to include an API management platform in your plans as you map out your AI-assisted software development strategy. DreamFactory’s iPaaS API platform offers all the features and convenience you need to manage your APIs effectively.

DreamFactory covers the entire API lifecycle, from automatic generation to documentation and version management. As technology marches on, AI tools and DevOps will become a regular part of the software development lifecycle. So you owe it to your team to have the best API management platform. 

Contact us to see how DreamFactory is the most comprehensive API management platform. Or, find out for yourself by starting your free 14-day trial today!

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