AI is transforming the way we approach software development, automating repetitive tasks, enhancing code quality, and saving developers valuable time. However, with tools like GitHub Copilot and other AI programming assistants on the rise, the question remains: is it all good news? 

Truth is, AI-assisted coding IS reshaping software development. As automated code generation becomes more advanced, tools powered by machine learning in coding help developers streamline workflows. In this article, we’ll dive into AI-assisted coding, examine the risks and rewards, and explore how teams can leverage AI for success without losing sight of the importance of human supervision.

What is AI-Assisted Coding and How Does it Work?

AI-assisted coding refers to the use of artificial intelligence algorithms to aid developers in writing, testing, and debugging code. Powered by machine learning models and natural language processing (NLP), AI helps automate tedious coding tasks, such as generating repetitive code structures or identifying potential bugs during code reviews. This frees up software engineers to focus on more complex problems.

What is an AI Coding Assistant?

An AI coding assistant, such as GitHub Copilot, acts as a tool that can write codes, offer code suggestions, and even complete entire functions for the developer based on context. These tools are trained on vast amounts of data, such as lines of code from public repositories, to provide highly accurate and context-sensitive code suggestions.

There are multiple AI programming assistants and tools for coding that use artificial intelligence (AI) and machine learning (ML) to enhance development processes. Examples include GitHub Copilot, Tabnine, and OpenAI Codex. These tools assist in writing efficient code, performing code reviews, and optimizing workflows for software engineers.

What Can Artificial Intelligence Do For Code Quality?

AI-assisted coding works by leveraging artificial intelligence technologies (ML, NPL and more) to analyze large datasets of existing code and predict the next steps in the coding process. Here’s how it typically operates:

  1. Training on Code Repositories: AI systems like GitHub Copilot are trained on massive amounts of public code data, sourced from platforms like GitHub, to learn common coding patterns, libraries, and algorithms. This training helps the AI understand the structure and syntax of different programming languages.
  2. Contextual Code Suggestions: Once trained, the AI tool can provide real-time suggestions based on the code a developer is writing. It examines the current context (e.g., the function, variable names, or code comments) and predicts the next lines of code or suggests completions for ongoing code snippets.
  3. Natural Language Input: Many AI coding assistants support natural language inputs, meaning developers can type a comment or request in plain English (or another natural language), and the AI generates the corresponding code. For example, typing “Create a function to reverse a string” might prompt the AI to write the appropriate function in the selected programming language.
  4. Bug Detection and Code Optimization: Some advanced AI tools can analyze code for potential bugs or inefficiencies, suggesting improvements. They can highlight problematic code based on historical data of common bugs or provide optimized versions of existing code.
  5. Code Reviews and Refactoring: AI-powered tools assist in performing code reviews, identifying inefficiencies, and suggesting code refactoring based on best practices. This streamlines the development process, especially for larger teams.

AI-assisted coding tools rely on continuous learning, where they improve their suggestions as they process more data and receive feedback from developers using the tool.

For example, GitHub Copilot, powered by OpenAI’s Codex, is trained on billions of lines of code, which enables it to offer contextually appropriate code snippets, complete functions, and fix errors in a wide range of programming languages.

AI-assisted coding ultimately helps developers save time, reduce errors, and focus on high-level decision-making. However, human oversight remains critical to ensure the code’s quality, security, and ethical standards are maintained.

AI-Assisted Coding: Risks and Rewards for Software Teams

While AI software development tools are revolutionizing how nearshore software engineers work, they are not without their limitations. Effective implementation requires balancing the speed and efficiency gains of AI with human oversight to ensure quality and ethical reliability.

The Benefits of AI in Software Development

AI-assisted coding brings several tangible benefits to software engineers and remote teams:

  • Cost effectiveness: By reducing manual coding tasks and improving code quality, AI tools offer significant cost savings.
  • Saves time: Tools like GitHub Copilot and OpenAI Codex can generate complete functions or resolve bugs, allowing developers to focus on high-level architectural decisions.
  • Improved code quality: AI tools perform constant code reviews, detecting inefficiencies and suggesting optimizations.
  • Streamlined development processes: AI integration with development processes enables faster iterations and more accurate lines of code.

For more on leveraging AI for software development, read this in-depth analysis: AI in Software Development: Boosting Innovation and Efficiency with AI Tools.

distributed software team performance: developer sitting in front of silver MacBook, looking thoughtfully at the horizon

Maintaining a balance between productivity and well-being is essential for distributed teams. Without the right balance, teams risk burnout, reduced morale, and decreased performance. Photo by Austin Distel.

Potential Risks: Why AI Needs Human Supervision

Despite its advantages, AI-assisted coding poses risks, especially when it operates without human oversight. Here’s why AI still needs human supervision and strategies to mitigate potential issues:

Lack of quality and accuracy

AI-generated code can be efficient but not always flawless. Machine learning models base their suggestions on pre-existing data, meaning they can inadvertently incorporate bugs or inefficiencies.

Regular code reviews by human developers remain essential to ensure accuracy and compliance with project standards.

Ethical risks and bias
AI systems, especially in machine learning (ML), can reflect biases present in the training data. Left unchecked, these biases can introduce ethical concerns or security vulnerabilities. Ensure AI programming assistants undergo audits for ethical reliability, as discussed in this article: How to Avoid AI Hallucinations and Ensure Ethical Reliability of Artificial Intelligence.
Limited context
AI tools can’t always understand the full context of a project. They lack the human ability to foresee long-term impacts of certain coding decisions. Pair AI suggestions with high-level human supervision to ensure alignment with project goals and performance needs.
Heavy dependency on data

AI tools rely heavily on the amount of data they are trained on. If the dataset is incomplete, the AI’s ability to offer effective solutions may be limited. Use large, diverse datasets for training, and employ ongoing monitoring and improvement of AI models.

For more insights, visit our resources on AI:

ai for software development: developers working hard - man sitting on chair wearing gray crew-neck long-sleeved shirt using Apple Magic Keyboard

Consider leveraging machine learning algorithms for code analysis, bug detection, and predictive maintenance, utilizing cutting-edge AI technology to enhance long-term software projects. This allows your team to dedicate more energy to creative problem-solving and innovation at a high level. Photo by Tim van der Kuip.

AI Programming Assistants: Which 5 Tools Lead the Market?

When it comes to AI programming assistants, choosing the right tool depends on your specific needs, programming environment, and workflow requirements.

In a nutshell, the most popular AI coding tools are:

Tool Features Pros Cons
GitHub Copilot Autocomplete, code generation, context-based suggestions Saves time, increases productivity Relies on public repositories for data, requires human review
Tabnine AI-powered code completion, privacy-focused Works locally for better security Limited free version
OpenAI Codex Code generation in multiple languages High-level suggestions for complex tasks Still in early development

These tools enhance developer workflows and have become integral to software team performance and collaboration. AI tools analyze patterns, generate boilerplate code, and offer suggestions for more efficient and secure programming.

Here’s a guide to help you decide which AI-powered coding tool fits best for various scenarios:

1. For General Purpose Coding: GitHub Copilot

  • When to use: GitHub Copilot is ideal for developers working across a range of languages, especially if you’re building web applications, APIs, or handling automation tasks.
  • How to use: Integrated directly into editors like Visual Studio Code, GitHub Copilot provides real-time suggestions as you code. It excels at auto-completing functions, generating boilerplate code, and giving smart suggestions for repetitive coding tasks.
  • Why: Copilot is trained on a vast dataset of publicly available code from GitHub repositories, making it particularly helpful for developers who need fast, contextual suggestions without leaving their IDE. Its seamless integration allows for smooth code assistance and refactoring.

2. For Java and Enterprise Applications: Codota/Tabnine

  • When to use: If your work involves enterprise-level applications, or you’re primarily coding in Java, Tabnine (formerly Codota) is a reliable AI assistant.
  • How to use: Tabnine can be integrated with several IDEs like IntelliJ IDEA, PyCharm, or Eclipse. It provides code completion, documentation lookup, and pattern-based recommendations, which can significantly enhance productivity when working with large codebases.
  • Why: Unlike GitHub Copilot, which uses open source data, Tabnine focuses on your local environment, improving over time as it learns your specific patterns and projects, ensuring privacy and customization in suggestions.

3. For Speed and Automation: Replit Ghostwriter

  • When to use: Replit Ghostwriter is ideal for quick prototyping, building small apps, or testing algorithms in an integrated environment.
  • How to use: Replit offers a browser-based IDE with AI-powered assistance through Ghostwriter. Use it to generate code quickly and saves time when developing lightweight applications or proofs of concept.
  • Why: With real-time collaboration features and a range of supported languages, it’s a great tool for rapid development or teaching environments where feedback and speed are crucial. Ghostwriter shines in fast-paced coding environments where quick iterations are needed.

4. For Machine Learning and Data Science: Kite

  • When to use: You actually won’t be able to use it — Kite is off the market. If you were developing in Python for data science or machine learning, Kite was ideal because it was built with ML workflows in mind.
  • How to use: It used to plug into popular data science environments like Jupyter Notebooks and PyCharm, where it offered smart code completions and recommendations based on your data science libraries like NumPy and Pandas. 
  • Why: It makes our list for the great lessons it taught, both as a technology and as a business. Check the link above to learn more on how it catared to data-heavy applications, offering predictions and auto-completions suited to ML and data science, making it perfect for developers working with machine learning models and algorithms.

5. For Secure Automation and Code Generation: DeepCode

  • When to use: If your goal is to automate code reviews and spot errors before they become bigger problems, DeepCode is a great fit. The emphasys is on secure development.
  • How to use: This AI assistant integrates with GitHub, Bitbucket, and GitLab, and uses machine learning in coding to analyze commits and spot bugs or potential issues based on historical data.
  • Why: DeepCode is designed for rigorous code reviews and spotting potential security issues, making it indispensable for large teams focused on building secure applications.
developer guide: man smiling and using MacBook on his lap

As you progress, you’ll also discover version control best practices, software maintenance strategies, and how to foster a culture of continuous improvement within your development team. Photo by Jud Mackrill.

Accelerate Your AI Endeavors

AI-assisted coding is a powerful tool that enhances the efficiency of software engineers, but it must be handled with care. Ubiminds can help you expedite your AI strategy by offering services such as:

  • AI Consulting
  • Machine Learning Operations (MLOps)
  • Machine Learning as a Service (MLaaS)
  • Computer Vision
  • Building your AI specialist team via nearshore software development or staff augmentation.

Want to get started? Let us know what you need and we’ll schedule a free consultation.

Quick FAQ for AI-Assisted Coding

  1. What is AI-assisted coding?
    AI-assisted coding involves the use of artificial intelligence to automate repetitive coding tasks, improve efficiency, and enhance code quality. It helps developers write, optimize, and debug code automatically, improving productivity.
  2. What is an AI coding assistant?
    An AI coding assistant, such as GitHub Copilot, helps developers by suggesting code snippets, identifying bugs, and improving productivity.
  3. Is there an AI for coding?
    Yes, tools like GitHub Copilot, Tabnine, and OpenAI Codex use AI to assist developers in writing, reviewing, and optimizing code. They are used to improve code quality, speed up development cycles, and reduce the chances of bugs.
  4. Is computer-assisted coding AI?
    Yes, computer-assisted coding refers to AI-enabled tools that help developers by generating code snippets, offering code completion, or identifying bugs. This form of AI leverages machine learning algorithms and can analyze large datasets to predict and suggest code patterns. It’s designed to enhance, rather than replace, human developers by handling repetitive tasks and improving coding efficiency.

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