In the ever-evolving world of technology, artificial intelligence (AI) is profoundly disrupting and transforming software development. Although this has been happening for some time now, the earlier integrations of AI into software development now seem almost quaint, thanks to the rapid ascension of generative AI. From the moment ChatGPT stormed onto the scene, the ability to prompt it to write working code is upending long-held development traditions and mores.
But what does all this buzz really mean for software development? In this technical exploration of AI software development, I cover the core concepts, mechanisms, and real-world applications with an eye to painting a picture of AI in software development after the ChatGPT dust has settled.
What is AI in Software Development and How Does It Work?
Before we explore its technical intricacies, let's dissect what AI in software development entails. At its core, artificial intelligence emulates human intelligence processes with algorithms and data. Some versions of AI automate human, manual tasks, others mimic cognitive functions associated with the human mind, and still others do both. When applied to software development, AI becomes a powerful tool.
AI software development automates a great deal of the repetitive and laborious aspects of building an application. Examples are code duplication, manual testing, creating and updating UI elements and layouts for multiple screens, and configuring build scripts. AI in software development also handles advanced problem-solving, data analysis, and intelligent decision-making, all of which are powered by the vast computational capabilities of machines.
Types of Artificial Intelligence Technology in Software Development
Now, let's get down to the nitty-gritty of AI technology as it pertains to software development. Here are some key AI technologies that are now part of the software development landscape.
AI Development Platforms
AI development platforms are comprehensive solutions that enable you to build AI applications from the ground up. They simplify the creation and delivery of AI models and applications by offering a toolbox replete with automation services and:
- Data preprocessing and cleaning tools.
- Machine learning (ML) algorithms and libraries.
- Model training and optimization capabilities.
- APIs for integrating AI models into applications.
- Production environments deployment and scaling options.
- AI model monitoring and management tools.
- Collaboration and version control features.
These platforms for AI development reduce the need for extensive coding, while enabling developers to customize and fine-tune AI solutions to specific use cases.
Deep learning, a subset of machine learning, revolves around neural networks inspired by the human brain. Learning directly from images, videos, text, and more, this technical marvel enables computer vision, natural language processing, and speech recognition in applications and software. These tools often rely on large datasets, powerful computational resources, and advanced optimization techniques to improve model performance.
How is deep learning used in application development? For one, deep learning models, such as GPT-3, can generate code snippets based on natural language descriptions. Developers can describe what they want to achieve in plain language, and the model can provide code templates or even complete functions or classes. Deep learning built into software development platforms can also review code for common errors, security vulnerabilities, and adherence to coding standards. For example, static code analyzers can use deep learning techniques to identify code patterns that often lead to bugs or security issues.
Machine learning is also the foundation for AI-assisted development. Models developed from millions of anonymized code patterns recommend the correct tools and patterns for any developer situation during the development process. This enables them to take the best possible next steps in their application design. Also as part of AI-assisted development, algorithms handle code generation, testing, and debugging. Users create logic flows faster and with more confidence, even for complex tasks.
Conversational AI is not just about chatbots; it's about the technical prowess required to create intelligent virtual assistants. This technology involves complex natural language processing, dialogue management, and user intent recognition. Conversational AI-powered chatbots or virtual assistants can help developers by providing code snippets, offering suggestions for solving coding problems, and generating boilerplate code based on natural language queries. They can be integrated into code editors and IDEs.
When part of a development platform, conversational AI can help developers find relevant documentation, tutorials, and code examples quickly. It can answer questions about APIs, libraries, and frameworks, making it easier for developers to learn and reference information. Along the same lines, it can provide new developers with onboarding guidance, tutorials, and answers to questions about coding standards and best practices. It can also offer personalized learning paths based on individual skill levels and goals.
The Technical Advantages of AI Tools for Software Development
The infusion of AI tools into software development brings an array of technical advantages that can significantly enhance the development process and user experience. Here's a technical breakdown of these benefits:
- Accelerated time-to-market: Technical teams benefit from AI-driven automation that expedites repetitive tasks and streamlines testing and deployment. This results in quicker product releases and a distinct competitive edge.
- Task automation: AI automates code generation, testing, and deployment processes. Rather than spending time on mundane, repetitive tasks, developers channel their energy into solving complex problems with innovative applications.
- Enhanced improved experience (via personal information): Personalization puts the technical prowess of AI firmly on display. Intricate algorithms analyze user behavior and preferences, resulting in tailored user experiences that boost engagement and retention.
- Predictive modeling: The heart of AI's technical power is predictive modeling. Using advanced algorithms and machine learning, AI can make data-driven predictions of next steps in logic flows, identify potential application issues, and anticipate user behavior and market trends.
Elevate Your Technical Game with OutSystems AI
The ongoing quest here at OutSystems is to make the lives of your developers and IT teams easier by handling the mundane, routine, and repetitive aspects of building apps so they don’t have to. To that end, OutSystems has infused AI throughout our high-performance low-code platform. Whether it’s having AI mentors guide them through development, adding AI to their apps, or both, your developers and IT teams encounter less friction and get more done faster. Let’s look at the two unique ways OutSystems does this.
AI Mentor System
The OutSystems AI Mentor System supercharges the expertise of developers with a revolutionary set of AI-powered guides called mentors. These mentors act as experts throughout the software development lifecycle, guiding, automating, and validating the work of developers. With the AI Mentor System, developers can be more productive and produce higher quality applications, a benefit that establishes that OutSystems is a clear step ahead in the low-code industry.
AI-Based Use Cases
OutSystems simplifies the implementation of AI in new applications, such as content generation, summarization, semantic search, and process automation, to name a few. Adapting existing applications to incorporate AI functions is also easier because the OutSystems low-code platform provides prebuilt components that seamlessly integrate with artificial intelligence models. Whether new or updated, your developers are free to deliver AI-powered apps inspired by industry-specific use cases. If you want to use the hottest AI trend in your apps, OutSystems provides generative AI integration to help your teams harness its potential to the fullest.
Into the Future of AI for Software Development with OutSystems
The next phase of AI-powered software development is one where developers will take center stage, delegating grunt work and tedious repetitive tasks to AI-powered mentors. For example, a suite of capabilities from OutSystems, currently in advanced stages of development, will use generative AI to transform conversational prompts provided by developers into fully functional applications. Your developers will be able to use natural language inputs to describe their desired applications so that a wider talent pool can contribute to app development. This transformative capability can redefine software development.
If your developers don’t have a clear picture of what they want to build they can share their intent, and OutSystems will provide suggestions and then accelerate the iteration cycle. There will also be a tool that learns from vast language models to provide intelligent suggestions. It will guide developers through next steps and even offer functionality ideas, based on accumulated knowledge. The end result will be applications with the same level of scalability, resiliency, and security as any other OutSystems-built app. This is just a taste of what’s on our AI-powered menu. There’s a lot more to come.
Change Your Software Development with AI
In conclusion, AI is a game-changer in software development. Its technical potential, from automation to predictive modeling, is reshaping the industry. For tech enthusiasts, embracing AI tools and technologies isn't just an option; it's an imperative for staying competitive in the digital age. OutSystems is committed to making sure that your developers and IT teams are using the best AI tech–inside our platform and out in the wild of their applications. Join us in this technical journey into the future of AI software development with OutSystems and usher in a new era of technology.
Takin Babaei is an accomplished go-to-market professional with over 15 years of experience. He has a passion for storytelling, strategic planning, and executing GTM strategies that enable sales teams to win new business. As a principal product marketing manager at OutSystems, he is currently applying his enthusiasm and expertise to providing compelling reasons to adopt high-performance low-code.See All Posts From this author