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How AI Tools Are Building Software Components in Record Time

How AI Tools Are Building Software Components in Record Time

The way we build software is changing fast. Developers once spent days or even weeks writing boilerplate code, migrating between frameworks, or debugging repetitive logic. Today, AI coding tools are stepping in to handle many of these time-consuming tasks.

According to Jellyfish’s 2025 State of Engineering Management report, AI coding tool usage surged from just 14% of pull requests (PRs) in June 2024 to 51% by May 2025. Teams using AI saw average PR cycle times improve by 16% compared to those without AI, translating to 13.7 hours saved per PR. Code quality also remained consistent, with no meaningful increase in bugs.

The increase in AI usage can be attributed to the huge leap in AI capability. These tools have gone from basic syntax suggestions to full-on code generation. At first, tools like Tabnine and Kite offered intelligent code completion. Then GitHub Copilot introduced prompt-driven coding. Now, we’re seeing a new wave of AI platforms that don’t just suggest code, but build components, refactor architecture, and even migrate entire codebases.

This evolution means developers no longer need to start from scratch or wade through mountains of documentation. Instead, they can describe the component or feature they want and let the AI do the heavy lifting.

How AI Tools Build Software Components

AI tools build software components by learning from billions of lines of public and proprietary code. With enough training data and fine-tuning, they can generate entire frontend components (buttons, forms, etc.), set up APIs and backend logic, automate testing and much more.

Want to build a login form with two-factor authentication? Describe it in natural language, and a modern AI coding assistant can scaffold the frontend component, set up backend API routes, and even suggest appropriate database models. Some platforms integrate directly with dev environments to allow for faster testing and debugging.

A prime example can be taken from component migration, a task that traditionally consumes significant dev hours. AutonomyAI’s CEO, Adir Ben-Yehuda, shared a case study during an interview with Eqvista, in which he describes the successful deployment of his company’s autonomous front-end coding platform to Deeto, an AI-powered customer marketing platform:

“One of our clients needed to migrate a substantial application from Angular to React—a daunting task that was estimated to take two months… We completed the migration in just five days. Not only was the turnaround remarkable, but the quality met production standards with minimal human revision. The client was thrilled, and it’s become one of our flagship proof points.”

Deeto helps businesses accelerate growth through customer storytelling. By cutting down the migration timeline from two months to just five days, it was able to roll out key features faster and without losing product momentum.

This kind of turnaround is becoming more common as dev teams integrate AI deeper into their workflows.

Balancing Speed With Context

The main advantage AI tools provide is speed. According to the Jellyfish report, 62% of engineering teams experienced at least a 25% boost in speed, while 8% claimed their output has doubled thanks to AI-assisted coding. This frees up a significant amount of developer time that can be redirected towards more high-impact and strategic tasks. 

Equally as important, this productivity boost has not come at the cost of code quality. The same report found no meaningful increase in bugs for teams using AI tools. That said, it’s still best practice to have skilled developers review AI-generated output, especially in critical systems or when dealing with sensitive data. 

That’s because while AI can generate production-grade code, it may lack enough business context to be able to make more informed architectural decisions. This also depends on the AI tool in use. Tools that are integrated directly into the development environment and have access to the full codebase, documentation, and workflows are generally better at producing context-aware output. 

Real-Life Success Stories

The impact of AI in software development isn’t just theoretical. Aside from AutonomyAI, there are many other examples of engineering teams benefiting from AI in their workflows.

Zoominfo, a leading Go-to-Market Intelligence platform, recently rolled out GitHub Copilot across its engineering organization of more than 400 developers. As a result, 20% of all new code comes directly from Copilot-generated content. At the same time, developer satisfaction has improved, with three-quarters of developers reporting that the tool has positively impacted their productivity.  

The financial giant, Morgan Stanley, has also implemented an in-house AI solution in an effort to modernize its COBOL-based legacy systems. Since its January 2025 launch, the tool has processed over 9 million lines of code, saving an estimated 280,000 developer hours.

Final Word

AI in software development is not an investment for the future, but for now. It’s already here, and real companies are seeing real results from integrating AI into their engineering workflows. In the not so distant future, we are likely to see AI agents embedded in every stage of the SDLC. 

If you’re not already exploring how AI can support your development process, you risk falling behind as the rest of the industry moves faster and delivers more with less.

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Community

Generative AI and Its Evolving Role in Software Development

Remember the days when software development was solely the domain of humans painstakingly writing lines of code? Those days are evolving rapidly. Generative AI, a branch of artificial intelligence capable of creating original content, is quickly becoming the co-pilot for software developers worldwide. This technology, leveraging advanced models like GPT-4, is not only automating mundane tasks but is also opening doors to unprecedented creativity and efficiency in the software development lifecycle.

 The Rise of AI Coding Companions

Generative AI models, like OpenAI’s ChatGPT or GitHub’s Copilot, have emerged as powerful allies for developers. These models can:

  • Generate Code: Need a function to sort a list? Just describe what you need, and the AI can generate the code for you.
  • Complete Code: Start typing a line of code, and the AI can suggest how to finish it, saving you keystrokes and brainpower.
  • Refactor Code: Want to clean up or optimize your code? The AI can suggest improvements.
  • Explain Code: Encountered a complex piece of code? Ask the AI to break it down for you in simple terms.
  • Detect Bugs: The AI can scan your code for potential bugs and suggest fixes, reducing the time spent on debugging.

Boosting Productivity and Creativity

The benefits of these AI coding companions are clear:

  • Increased Efficiency: Developers can accomplish tasks much faster, from writing boilerplate code to debugging complex issues.
  • Enhanced Creativity: The AI can offer alternative solutions or suggest innovative approaches, sparking new ideas.
  • Reduced Tedium: Developers can focus on higher-level problem-solving instead of getting bogged down in repetitive tasks.
  • Accelerated Learning: Newcomers can learn faster by getting instant feedback and explanations from the AI.

Real-World Applications

Generative AI isn’t just a theoretical concept; it’s already being used in the real world:

  • GitHub Copilot: This popular tool integrates with various code editors, providing code suggestions and completions in real time.
  • Tabnine: Another AI-powered code completion tool that supports multiple languages and frameworks.
  • Replit Ghostwriter: This tool offers AI-powered code generation, completion, and transformation features.
  • Deep TabNine: A deep learning-based code completion tool that can be integrated with various IDEs and text editors.

Challenges and Considerations

While the potential of generative AI is exciting, there are also challenges to address:

  • Accuracy: AI-generated code might not always be perfect and may require human review and correction. While these tools can significantly speed up the coding process, developers should still verify the AI’s suggestions to ensure they meet required standards and project specifications.
  • Bias: AI models can inherit biases from their training data, leading to potentially biased or unfair code suggestions. This requires developers to remain vigilant and critically assess any suggestions made, ensuring equitable and inclusive coding practices.
  • Security: The security of code generated by AI needs careful consideration to prevent vulnerabilities. Developers must be aware of potential security gaps and rigorously test AI-generated code to protect against cyber threats and maintain the integrity of their applications.
  • Ethics: As with any technology, the ethical implications of AI in coding should be carefully evaluated and addressed. This entails considering the broader impact of AI-generated solutions and ensuring that their use aligns with ethical standards and promotes positive societal outcomes.

The Future of AI-Assisted Development

The future of AI software development services is undoubtedly intertwined with generative AI. As these models continue to improve, we can expect even more sophisticated tools that will:

  • Understand Natural Language Better: Allowing developers to communicate with AI in a more intuitive way. As natural language processing capabilities advance, developers will be able to describe the functionality they need in plain English, and the AI will generate the corresponding code, reducing the need for detailed programming knowledge.
  • Generate More Complex Code: Tackling larger, more complex programming tasks. Future AI models will be capable of handling intricate logic, cross-functional dependencies, and larger codebases, thus enabling the automation of more sophisticated software projects.
  • Integrate with More Development Tools: Becoming a seamless part of the developer’s workflow. As generative AI tools continue to evolve, their integration with a wider range of development environments, version control systems, and project management tools will ensure a smoother and more cohesive development experience.

The Developer Nation Survey, a comprehensive look at developer trends, already highlights a growing interest in AI tools for coding. This indicates a shift in how developers perceive and use AI, moving from skepticism to embracing its potential.

Conclusion

Generative AI is a game-changer for software development, offering a glimpse into a future where humans and AI collaborate to create more efficient, innovative, and secure software. While challenges remain, the potential benefits are too significant to ignore. As we move forward, developers who embrace these AI-powered tools will be well-positioned to thrive in the ever-evolving landscape of software development.

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Community

AI Tools in Development: A Comprehensive Guide

Artificial intelligence is one of the biggest buzzwords in recent times. AI this, and AI that. It’s everywhere! 

As a developer, you’re probably familiar with this novel technology that has the potential to change the way you work. In fact, the numbers say it all – 63% of developers engage with AI-assisted development.

In this guide, we’ll tackle the importance of AI tools in development. Plus, we’ll cover its benefits and why adding it to your digital arsenal can help you excel as a developer.

A Developer’s Perspective on Artificial Intelligence

Before anything else, let’s take a look at how this technology affects developers. For them, artificial intelligence serves as a challenge and an opportunity. 

It’s a challenge as it introduces the concepts of machines learning from data, when traditional programming concepts are based on explicit instructions. This shift challenges developers to adopt a new thought process – one where algorithms can continuously evolve.

At the same time, it represents a massive opportunity by allowing them to innovate and solve problems quickly. Suddenly, the issues that couldn’t be solved by traditional programming are now within reach, thanks to the advancement of artificial intelligence.

The Most Popular AI Tools in Development

As a developer, you must learn to roll with the punches in the AI sector, given its fast pace of development. Doing so will require you to work with cutting-edge AI tools that help streamline your workflow. Here are some of the most popular AI tools that you can use as a developer:

Code Generation – GPT 3

GPT-3 is arguably one of the most popular AI tools that you can use as a developer. It was developed by Open AI in the pursuit of creating smart and trustworthy artificial general intelligence systems.

This tool excels in generating code. With its ability to understand various contexts and programming languages, it can provide accurate and relevant code suggestions. 

If you’re looking for a language model that generates code snippets, predicts code, refactors it, and helps you with algorithmic writing, then this is one of the tools that can fit the bill.

Code Analysis – Deep Code

To improve the quality of your code, Deep Code can do the job. By employing machine learning algorithms, it learns from a vast source of data to recognize patterns and gain insights into the best coding practices – all while reducing errors.

Perhaps one of its biggest strengths is its ability to provide context-aware suggestions, as compared to solely relying on rule-based analysis. This helps Deep Code make more nuanced recommendations for developers.

Natural Language Analysis – DialogFlow

Chatbots have found their place in many industries. In finance, chatbots can be used in private equity fund administration. In healthcare, they can be used to assist in the diagnosis of diseases. 

Developers lead this effort by integrating natural language processing (NLP) capabilities into applications with tools like DialogFlow. With its user-friendly interface, developers can easily customize how websites and applications respond to different queries.

Project Management – Trello

Trello has always been one of the leading project management tools across all industries. This tool allows you to add what you call ‘Power-Ups’ – features that you can integrate into your boards on Trello. 

With the boom of artificial intelligence, there have been Power-Ups that make use of AI technology. Notes & Docs, for example, is an AI-powered tool that can help you take down notes in a much more efficient manner – whether it be through summarizing them, simplifying them, or proofreading them.

Testing and Quality Assurance  – Selenium

Before you release a web application, it must undergo adequate testing and quality assurance. Selenium is one of the AI-powered tools that can deliver in this aspect. 

There are a few products that support the integration of AI with Selenium: headspin, Healenium, and testRigor. All of these boast unique features that upgrade Selenium’s capabilities when it comes to test automation. For example, Healenium uses machine learning algorithms to manage and modify web components.

Benefits of AI Tools in Development

Now that we’re aware of the role of AI tools in development, let’s have a quick rundown of the benefits they provide:

  • Higher Productivity: By automating repetitive and routine tasks in web development, artificial intelligence tools can help developers focus on more complex aspects of their work.
  • Cost Efficiency: The cost benefits are endless – automated testing, code analysis, and bug detection all reduce the need for extensive testing, saving a huge amount of resources.
  • Advanced Data Analysis: AI excels at analyzing vast amounts of data at high speeds and precision. Through this, developers can make data-driven decisions throughout the development workflow.

Final Thoughts

That being said, the role of artificial intelligence in development is indeed significant. 

Whether it’s generating new code or testing it for quality, AI tools are capable of assisting developers in these tasks. Not to mention, several benefits come with using AI-powered tools – one being increased productivity.

One thing’s for sure. If developers want to use AI to its fullest potential, they have to stay up to date with the latest developments, as this is the key to pushing the boundaries of development even further.