Software Development has greatly benefited from Generative Artificial Intelligence (GenAI).
In fact, a recent study by the McKinsey Institute showed that developers could complete tasks up to twice as fast by using Generative AI tools.
That’s why companies like Microsoft and Amazon have harnessed GenAI to build tools dedicated to boosting and improving Software Developers’ capabilities.
Currently, ChatGPT and GitHub Copilot can write code in several different programming languages, libraries, and frameworks.
That's quite useful for enhancing productivity in Software Development!
Without further ado, let's explore the main advantages of Generative AI in Software Development practices!
How Can GenAI Improve Software Development?
First, GenAI tools can enhance Software Developers by allowing them to increase productivity and efficiency at work.
This mindset can be paramount for fast-growing businesses looking to accelerate time-to-market for their digital products!
What’s more, these tools can greatly improve code quality and team performance while reducing developer turnover.
Additionally, IBM states that GenAI tools can understand context to generate, adapt and improve code based on feedback.
Yet, it’s always advisable to build digital products with human-based procedures to guarantee seamless digital products.
Pros of GenAI Tools in Software Development
1. GenAI for Code Generation
GenAI coding tools can generate boilerplate code, a great starting point for devs to focus on more creative and complex tasks.
Likewise, GenAI tools are useful for creating templates, functions and classes.
Developers can later use these to build menus, login screens and other straightforward operations.
Nonetheless, GenAI tools can still miss the fact that the code must make sense across the entire process, not just a single file.
At the time of writing this article, AI-generated code is not being 100% reliable.
Software Developers should always conduct careful code reviews.
2. GenAI for Code Suggestions
By analyzing existing codebases, GenAI tools can provide meaningful and context-aware suggestions and completions.
Real-time code suggestions provided by GenAI tools could consider the project's architecture and style conventions.
Dvelopers who use these tools can either generate new alternatives or only accept partial suggestions.
3. GenAI for Code Learning
GenAI tools can also use NLP to gain a deeper understanding of large codebases.
The best part is that Software Engineers can take advantage of conversational interfaces to understand the code themselves better.
Surveys revealed that 49% of developers improved their own codebase understanding thanks to AI.
4. GenAI for Refactoring
Some modern Generative AI tools can perform time-consuming and repetitive coding tasks.
For instance, they can refactor code with remarkable accuracy.
They can eeven use a secondary AI to fact-check the refactored code.
Tools like Amazon Q Developer even have a refactor command to ease code refactoring optimizations with a single click.
Amazon Q Developer offers a unique feature to help developers upgrade their products from Java 8 to Java 17 in record time.
5. GenAI for Software Testing
In the context of Software Testing, Gartner states that AI can enhance planning, prioritization visual testing and test data generation.
Likewise, PwC experts claim that GenAI can generate tests for a wider range of scenarios than development teams typically cover.
GenAI tools can ease Regression Testing and automate the generation of test scripts.
They can also help developers write unit, functional and even security tests, leading to better test coverage.
6. GenAI for Debugging
With Machine Learning, Generative AI-based tools can analyze hundreds of lines of code and help find and fix bugs.
That's one of the most time-consuming tasks of coding.
A study conducted at Cambridge University showed that ML for bug detection could help identify 97% of errors.
These tools can analyze analyze historical data to anticipate where bugs are likely to appear.
Likewise, GenAI tools harness Natural Language Processing to interpret bug reports more naturally and understandably.
7. GenAI for Documentation
GenAI coding tools can assist developers in writing accurate documentation and comprehensible comments.
They can also monitor the codebase constantly to update the documentation based on changes.
As a result, the documentation will always reflect the current state of the codebase.
8. GenAI for Collaboration
Generative AI tools such as GitHub Copilot can explain how code snippets work and write pull request summaries.
This feature eases the understanding of the purpose of code changes that were made by others to improve real-time collaboration.
Teams at companies like Figma have reported it has helped them collaborate more effectively.
Conclusion
GenAI in Software Development can automate routine tasks, improve collaboration and detect bugs easier.
As a Full-Cycle Product Development agency, we're aware of how GenAI tools can impact the future of Software Development workflows.
Feel free to reach out if you'd like to hear about our unique approach to building even the most disruptive software products.
