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date icon October 10, 2024

How AI decreases the work of programmers with examples?

CEO & Founder at CodeOp

I know the tech atmosphere lately has been a bit predatory. But here’s the truth, AI isn’t coming to take your programming job; it’s here to make your life easier.

With AI advancing rapidly, you no longer need hours dealing with repetitive tasks like debugging, code refactoring, or even generating documentation.

The key shift we’re seeing is that AI doesn’t aim to replace developers but to streamline processes, allowing you to focus on the job’s most complex and creative parts.

Gone are the days of painstakingly writing boilerplate code or manually scouring for bugs.

Today, AI tools can assist with everything from writing code snippets to automating testing procedures. And the impact? More time for innovation and solving the big problems that matter.

1. Automating Code Generation

AI-powered tools like GitHub Copilot and OpenAI’s Codex can now assist in writing entire blocks of code based on a simple prompt.

Imagine typing a few comments or describing a function, and the AI auto-generates clean, functional code within seconds.

For example, instead of manually writing out every line of code in a CRUD (Create, Read, Update, Delete) operation, tools like Copilot can predict and produce most of the structure, freeing you from repetitive tasks.

While AI can’t handle complex logic or architectural decisions, it excels at automating simpler code, making your workflow smoother and more efficient.

Here’s an excellent paper by Arghavan Moradi Dakhel et al, titled “GitHub Copilot AI pair programmer: Asset or Liability?”

2. Debugging and Error Detection

Instead of spending hours going through logs and code to find the root of an issue, AI can pinpoint potential bugs in minutes.

Tools like DeepCode, Snyk, and Tabnine are great examples of AI-driven platforms that help identify vulnerabilities and logical errors in code. These tools scan the codebase, compare it against millions of other code snippets, and flag common security loopholes, syntax errors, and bad coding practices.

For example, DeepCode’s AI uses machine learning models to identify codebase patterns, flagging inconsistencies or vulnerabilities far faster than a human could. It helps companies like Siemens and BMW maintain high code security and efficiency standards, slashing debugging time by up to 40%.

3. Automated Testing and QA

AI-powered tools can automatically generate test cases, detect bugs, and even predict where problems might occur based on historical data.

Tools like Testim, Functionize, and Applitools use AI to automate functional, regression, and performance testing, significantly reducing the manual effort required.

For instance, Applitools leverages visual AI to compare screenshots of a web application to identify layout or UI discrepancies, making visual testing faster and more reliable.

Large companies like Intuit use Applitools to run visual regression tests on TurboTax’s over 50 UI components and screen configurations across browsers and viewport sizes.

4. Documentation Generation and Management

One of the most mundane yet necessary tasks for developers is writing documentation.

Whether explaining code functions, writing API docs, or detailing system architectures, documentation is critical for maintaining codebases and ensuring other team members or users understand how the software works.

Tools like Kite and NaturalDocs can auto-generate documentation by analysing code comments, function names, and even the logic within the code.

GitHub Copilot, for instance, can write basic function descriptions while generating code, making it easier for developers to maintain well-documented codebases.

5. AI in DevOps Automation

DevOps automation traditionally involves tasks like continuous integration/continuous delivery (CI/CD), infrastructure provisioning, and monitoring.

AI takes it further by predicting system failures, optimising cloud resources, and automating repetitive tasks that slow the development cycle. Tools like Jenkins, Ansible, and Kubernetes increasingly incorporate AI-driven automation to manage complex deployments and scalability.

For example, AI algorithms can dynamically predict server load and scale resources, ensuring optimal performance without manual intervention.

6. AI in Natural Language Processing (NLP) and Chatbots

AI-powered Natural Language Processing (NLP) has given rise to intelligent coding assistants and chatbots that provide real-time support for developers. These tools can answer questions, suggest code snippets, and even debug issues by analysing natural language inputs.

Tools like GitHub Copilot, OpenAI’s Codex, and Tabnine leverage NLP to understand the context within code and offer meaningful suggestions.

Developers can now simply describe what they want to achieve in plain English, and AI models can generate the necessary code.

For instance, with GitHub Copilot, you can type, “create a function that sorts an array of integers,” and Copilot will instantly generate the function.

7. Reducing Development Time for MVPs and Prototypes

AI is particularly beneficial in accelerating the development of Minimum Viable Products (MVPs) and prototypes, a phase where speed is crucial to validate business ideas.

Traditional development cycles for MVPs often require several weeks or months, but AI tools can drastically reduce this timeline.

AI-driven code generators like DeepCode and platform solutions like Bubble (which uses AI to assist with no-code/low-code development) allow non-technical founders or developers to quickly create functional prototypes.

By automating boilerplate code and generating the foundational logic, AI enables teams to focus on testing and refining core features.

Author: Katrina Walker
CEO & Founder of CodeOp,
An International Tech School for Women, Trans and Nonbinary People
Originally from the San Francisco Bay Area, I relocated to South Europe in 2016 to explore the growing tech scene from a data science perspective. After working as a data scientist in both the public...
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