Developers are “the first group to adopt AI at work,” according to GitHub. By mid-2023, 92% of developers already reported using AI in software development. And by and large, they’re optimistic about AI’s potential. Their survey found that:
- 81% think AI coding tools will help them collaborate better
- >50% believe AI tools will help them shift their focus from repetitive tasks to higher-value problem-solving
- Close to 50% think AI will help software engineers focus more on solution design and innovation
The future of AI in software development looks promising, and it’s a future of AI for developers—not AI that replaces developers.
AI-driven technology enhances programmers’ capabilities, automating routine tasks and providing intelligent assistance to streamline and speed up software development processes. AI tools, called copilots, are already transforming how developers code, test, and manage projects, increasing productivity, efficiency, and innovation.
Read on to learn the benefits of AI for software engineering and several AI use cases in software development. We’ll also discuss how the advent of generative AI in software development unlocks more inclusive and ethical software development processes.
Enhanced code generation and auto-completion
Writing code is typically the longest part of the software development process. Without AI assistance, code writing can take months. While Integrated Development Environments (IDEs) have features that help engineers write code more efficiently, the game-changing difference is that AI copilots are proactive and context-aware.
Using AI, copilots can auto-complete or generate code that satisfies requirements and integrates seamlessly with the existing codebase. Developers can now turn to AI to suggest entire code snippets, functions, or even complex algorithms—which will be syntactically correct and optimized for performance.
Our own AI Copilot program helped 65% of software development teams raise productivity by 25%. Results included:
- 53% greater focus on task completion
- 81% less frustration related to routine tasks
- 79% more speed in information retrieval
- 51% more efficiency in code review
AI Copilot technology holds immense promise for engineering services. Our findings demonstrate that with the AI Copilot, developers can work better and faster by delegating routine tasks and task prioritization to the AI Copilot.
To learn more, download our AI Engineering Productivity Cookbook. This comprehensive guide details various ways to use AI to enhance engineering productivity. We explore multiple aspects of AI integration in software engineering workflows, focusing on practical applications and real-world examples.
Evolving support across languages and frameworks
As part of ongoing development and enhancement, AI copilots continually expand their support for different programming languages and frameworks. That means developers can use AI for software development on all kinds of projects, from web development and mobile apps to enterprise software. AI tools can also help all levels of coders, from hobbyist programmers to enterprise software engineers, gain efficiency and productivity.
While the technological landscape is changing faster than ever, you can be confident that copilots will continue to cover mainstream languages and frameworks and accommodate niche or emerging ones.
AI-powered testing and debugging
Quality engineering control is a critical aspect of the software development process. Verification and validation activities need to occur throughout the software development lifecycle, with final testing traditionally taking 3–6 weeks, according to Forbes.
Since AI tools excel at automation use cases, testing and debugging are perfect examples of how to use AI in software development. AI copilots can automate the generation of test cases, ensuring comprehensive coverage of various scenarios and edge cases. This automation saves developers a lot of time, but that’s not all. AI tools also help catch bugs that might go unnoticed by human programmers, improving the reliability of the resulting product.
“Code always needs to be tested,” says David Gewirtz at ZDNet. “The typical cycle involves a bit of coding, a bit of testing, a bit of fixing… rinse, wash, repeat. However, because AIs may be able to predict software behavior, they could flag errors before testing even needs to begin.”
Moreover, AI tools can do more than find bugs. It also helps with debugging by quickly identifying and suggesting fixes for errors. AI can analyze your historical codebase and predict where bugs are likely to occur in the future. This further streamlines the debugging process and reduces downtime.
Natural Language to code conversion and support
One of the most significant advancements of AI for software development is Natural Language Processing, or NLP. With the power of generative AI, new tools can translate plain language descriptions into syntactically correct and efficient code.
The impact of natural language processing goes beyond technology itself. With AI assistance, programming will become more accessible and inclusive. How? Well, until now, software development has had an inclusivity problem. Due to unconscious biases and a lack of diverse representation on engineering teams, software has often failed to meet the needs of broader user populations. The usual answer to software’s diversity problem has been to get more diverse talent up to speed with coding boot camps.
With generative AI for software engineering, creating an inclusive software development team just got a lot easier. NLP opens the doors for more stakeholders to contribute to the software development process without needing to learn to program first. For instance, a project manager could describe a desired feature in simple terms, and the AI copilot would generate the corresponding code. Generative AI tools bridge the gap between technical and non-technical team members, fostering a more collaborative environment.
AI-driven project management and planning
AI-assisted development tools do more than code generation. For example, GitHub Copilot integrates with hundreds of project management apps to facilitate real-time adaptation within project workflows. The tool can offer timely suggestions for adjusting project scopes and timelines based on new developments and emerging priorities.
This dynamic approach to project management is truly the future of software engineering with AI. The copilot ensures teams can respond swiftly to changes, maintain alignment with business goals, and deliver projects on time. Insights and recommendations from AI copilots can help project managers optimize resource allocation and streamline processes, cutting costs and helping ensure project success.
Intellias utilized AI-driven project management tools to dynamically adjust project timelines and resource allocation for a complex software development project in e-learning. This adaptability resulted in a 20% increase in project efficiency and on-time delivery. Read more about this fantastic result in our case study: Intellias Boosting Development of an E-Learning Platform.
Seamless integration of AI into development environments
While some industries struggle to develop good AI use cases, there are endless AI use cases in software development—especially with generative AI. Consider how often engineers have to juggle many different tools and platforms. Various aspects of the software development lifecycle, such as coding, debugging, testing, and documentation, are typically handled by different specialized tools. AI for developers can improve the process at every step.
- Coding and IDEs: AI-driven copilots boost productivity with advanced auto-completion and context-aware suggestions, generating code snippets based on natural language descriptions. Developers can code more efficiently and accurately without leaving the IDE.
- Debugging and Testing: Developers often have to switch to separate tools or plug-ins, for example, running tests in one tool, analyzing the results in another, and then going back to the IDE to resolve issues. AI tools streamline debugging and testing by providing inline suggestions and flagging potential issues in real-time.
- Documentation and Research: Gone are the days of frequently leaving the coding environment to look up documentation or search for code samples. Copilots use AI to bring a wealth of knowledge right into the IDE. It offers code suggestions and explanations to keep developers focused and maintain a steady workflow.
- Version Control: Developers often have to use separate tools or command-line interfaces to commit changes, resolve conflicts, and manage branches, adding layers of complexity to their workflow. For example, GitHub Copilot integrates directly with GitHub, simplifying version control.
- Collaboration: AI tools foster better collaboration throughout the software development process by providing consistent coding patterns and suggestions across the team. It helps ensure code quality and uniformity, which makes it easier for a team of coders to understand and work on each other’s code.
Looking into the future of AI in software development, we believe AI will lead to a new era of seamless teamwork in all-in-one Copilots with enhanced collaboration tools. These AI tools will enable real-time code collaboration, intelligent merging of code changes, and suggestions for conflict resolution. Integrating AI into a collaboration framework will make software development more efficient, with smoother processes for managing and integrating contributions from multiple team members.
Perhaps most importantly, using AI helps bridge the gap between different disciplines within development teams. Breaking down silos between software developers, data scientists, designers, and domain experts will support a more cross-disciplinary practice, helping teams include diverse perspectives. That diversity fuels innovative solutions and better outcomes for the diverse users of your products.
We envision a more streamlined, inclusive, and innovative development process. With AI tools that invite people of all skill levels to get involved with development, teams can create a collaborative process that yields higher-quality software with a faster time to market.
What can you accomplish with AI in software development?
AI copilots are changing the nature of software development, from enhanced code generation and debugging to dynamic project management and seamless integration into development environments. The transformative potential of AI in software development is immense, and this technology will impact all types of software development projects. Growing cross-language support and natural language processing will continue to make enhanced code generation and auto-completion tools applicable to a wider array of software projects. This universality means that developers with any technology stack will benefit from AI assistance.
“My advice would be to embrace the interdisciplinary nature of AI,” says Dr. Pablo Rivas, Baylor University Assistant Professor of Computer Science. “Don’t just focus on the technical aspects; understand the ethical, social, and business implications.”
By embracing these advancements, you can boost your developers’ productivity, include more viewpoints for better innovation, and maintain your competitive edge. What does the future of software engineering with AI look like for your company? Let’s build it together. To find out more, contact our experts today.
