Discover how AI and ML can potentially change the software development industry, and how AI affects software development and minimizes developers’ workload
Software development is a long, complex, and expensive process. Business owners – and developers themselves – constantly seek ways to optimize it. Good news for you, using artificial intelligence (AI) and machine learning (ML) is becoming increasingly popular in that regard.
According to a recent survey by Gartner, AI and ML are some of the trends that will shape the future of software development. For instance, early 73 percent of adopters of GitHub Copilot, an AI-driven assistant for engineers, reported that it helped them stay in the flow.
The use of this tool resulted in 87 percent of developers conserving mental energy while performing repetitive tasks. That increased their productivity and performance.
Twinslash and other software vendors and developers, on other hand, build AI-driven tools to help engineers with testing, debugging, code maintenance, and so on.
So: let’s learn more about AI and ML and their impact on software development.
AI Will Solve Typical Software Development Challenges
The ability to automate monotonous manual tasks is one of the significant benefits of AI. There are several ways to effectively implement AI in the development process that completely replace human intervention – or, at least, reduce it enough to remove the tediousness of repetitive tasks and allow your engineers to focus on more critical issues.
Error Management With AI
One of the common applications of AI in development is utilizing it to reduce the number of errors in the code.
AI-powered tools can analyze historical data to identify recurring errors or faults, spot them, and either highlight them for developers to fix or fix them independently in the background. The latter option will reduce the need to roll back for fixes when something goes wrong during your software development process.
Software Testing Automation
AI improves the quality, coverage, and efficiency of software testing. This is because it can analyze large amounts of data without making mistakes. Eggplant and Test Sigma are two well-known AI-assisted software testing tools.
They aid software testers in writing, conducting, and maintaining automated tests to reduce the number of errors and boost the quality of software code. AI in testing is extremely useful in large-scale projects – usually combined with automated testing tools, it helps to check through multi-leveled, modular software faster.
Software Design Improvement
ML software can track how a user interacts with a particular platform and process this data to pinpoint patterns that can be used by developers and UX/UI designers to generate a more dynamic, slick software experience.
AI can also help discover UI blocks or elements of UX people are struggling with, so designers and developers can reconfigure and fix them.
Security Enhancements with AI
Code security is of utmost importance in software development. You can use AI to analyze data and create models to distinguish abnormal activity from ordinary behavior. This will help software development companies catch issues and threats before they can cause any problems.
Apart from that, tools like Snyk, integrated into engineers’ Integrated Development Environment (IDE) can help pinpoint security vulnerabilities in the apps before releasing them in production.
3 AI Trends That Will Transform Software Development
Let’s talk about the main overall trends that are changing the field of software engineering and product development.
1. Generative AI
Generative AI is a powerful technology that uses AI algorithms to create any kind of data — code, design layouts, images, audio or video files, text, and even entire applications. It studies datasets independently and can help produce a wide range of content.
One of the most significant benefits of generative AI is that it can help developers create software quickly and efficiently. For instance, it assists with:
Code completion. AI-enabled code completion tools in IDEs, such as Microsoft’s Visual Studio Code, can help developers write code faster. For VS, such a tool is called IntelliCode – it analyzes a ton of GitHub repos and searches for code snippets that might be relevant for the developer’s next step and completes the lines for them.
Layout design. AI-powered design tools can analyze user behavior and preferences to generate optimized layouts for websites and mobile applications. For example, for some AI-powered plugins on the design platform, Canva uses machine learning algorithms to suggest layouts, fonts, and colors for marketing materials.
(Entire) app development. With generative AI, developers can automate the process of creating software or pieces of software by telling the AI the prompts for an app one wants to build. OpenAI’s Codex can do that, using natural language processing models both for parsing through conversational language and syntax of a programming language.
2. AI in Continuous Delivery
Continuous delivery is a software development practice where code updates are automatically built, tested, and deployed to production environments. AI-powered continuous delivery can optimize this process by using machine learning algorithms to identify and address issues before they become critical.
Machine learning algorithms can analyze the performance of production environments and predict potential issues before they occur, reducing downtime and improving software reliability.
Apart from that, ML can parse through different deployment strategies and recommend the best approach based on past performance and current conditions of the system.
3. AI-Powered Roadmap Planning
Now, that trend isn’t directly tied to software development, but it impacts it quite significantly. Product and project managers can use AI tools to plan the project faster.
Of course, tools like ChatGPT won’t replace the experience of talking to actual potential users, but it can still help them quickly get a grasp of the market situation, trends, or common concerns users have with the competitor’s product.
Tools like that one can also be utilized to conduct drafts for SWOT analysis, which is also extra vital for planning out the value proposition of the software and prioritizing features-to-be-built for a roadmap. Now, ChatGPT is also a generative AI, but we thought that its application deserves a separate section.
Final Thoughts
As Eric Schmidt, former CEO of Google, once said, “I think there’s going to be a huge revolution in software development with AI.” That revolution is now. It is safe to say that the future of software development lies in AI and ML.
With the rise of AI-powered programming assistants and AI-enabled design work and security assessments, software development will become more cost-effective. Utilizing AI and ML in software development will also increase productivity, fasten time-to-market, and improve software quality.
