For all the promise and anxiety about artificial intelligence in the workplace, some of AI’s greatest enhancements to productivity are in AI’s own nursery: software development. Many software developers are increasingly comfortable integrating AI tools into their workflows, particularly when it comes to automating repetitive or low-level coding tasks.
The history of development tools has been a relentless pursuit of more intuitive integrated development environments (IDEs) that enable developers to visualize and manipulate code in more “human” terms. AI in software development is like the code meeting the developers half-way; translating developers’ questions and commands into granular, code-level outputs.
As a result, AI is driving improved CI/CD (Continuous Integration and Continuous Deployment) to accelerate everything from bootstrap projects to v.N enhancements and yielding lighter, more bug-free products in the process. It is also producing better documentation that benefits the whole team moving forward and increases the valuation of the companies overseeing it.
From Notion to Prototype
The sheer number and specificity of AI tools in software development is a testament to their appeal and the range of their potential. The first-place developers might engage AI is at the pseudocode level, describing the desired operations or functionality and letting the AI “sketch” the code blocks.
With those suggested design patterns and maybe even a few code snippets, the developer is ready to start coding the final product. Rarely is every line of a product entirely new. So AI might make real-time code suggestions or offer context-aware code completions for standard data calls and CRUD (Create, Read, Update, and Delete) operations to speed up the process.
Where new or unconventional functionality is the goal, AI can help identify and replicate newly introduced coding patterns across the codebase for consistency and future use.
Testing, Testing, 1-2-Go
Once the prototype is functional, the AI can really do what it does best: optimize. In addition to automating testing processes for basic functionality, AI can suggest edge cases and potential issues. In fact, it’s an ideal tool for highlighting syntax errors and suggesting fixes.
Likewise, AI is perfectly suited for complex refactoring chores, optimizing the code for executional efficiency. When bugs appear in the new functionality, AI can analyze implemented solutions to flag potential regressions, deprecated usage, or conflicting logic before integration.
Beyond the core functionality, AI can help identify security vulnerabilities and potential I/O or other issues that might otherwise only present themselves once the code is deployed. This would be an excruciating process even with other forms of automation. AI’s “training” can recognize a wider range of anomalies and, sometimes, even propose solutions.
Documentation: Putting It in Writing
AI is rightly famous for its Natural Language Processing (NLP) capabilities. Once the developer and their AI assistant emerge from the depths of the code, AI can put those skills to work in preparing the documentation. Strangely, this can be the hardest part for a developer to do well. But it’s a snap for AI.
AI can generate initial documentation based on code structure and inline comments using its natural language processing capabilities. Once the AI has made its pass, the developer can proof the AI’s work for clarity, understanding what future developers might need to know if and when they wade back in. After the code itself, this documentation can be the most valuable IP the company has, since it makes that code a more leverageable asset.
Continuous Learning and Virtual Guidance
AI is now so much a part of the development process that the tools and developers who use them are becoming symbiotic. When developers aren’t building products, they’re creating tools and libraries for AI to use moving forward. Some of these focus on more refined coding capabilities and some enhance the usability of the tools themselves.
Some AI serves only to help coders learn new languages faster or in greater depth. Other AI tools do the same for the whole range of AI tools. This degree of “hand-holding” empowers developers to keep their skills current and the products they produce constantly competitive.
This perpetual re-education has always been critical for developers to sustain their careers. Far from taking away programming jobs, the new frontier of AI-assisted development is making programmers and their products better and extending that dynamic into the foreseeable future.
Solvo: Your Enhanced Dev Team
Solvo developers use a range of AI tools to speed up development with the highest quality code possible. The fact that Solvo is also a significantly cost-effective nearshore resource is just the icing on the cake.
Need to extend your software development capabilities? Get productivity boosts from day one by hiring people who use AI like a second brain. Contact Solvo Global today to explore customized outsourcing solutions tailored to your business needs.
