Table of Contents
- Why are AI Code Generators Getting Popular?
- Benefits of AI Code Generator
- Top Five AI Code Generators
- Limitations of Using AI Coding Tools
In a world driven by innovation and efficiency, AI code generators have emerged as game-changers in software development. These ingenious algorithms possess the power to transform complex ideas into elegant lines of programming, making the once-daunting task of coding more accessible to both novices and experts alike. Using these tools, data scientists, developers, and non-developers can automatically generate lines of code in different programming languages from a simple prompt. These generators can assist developers in various stages of the software development process—from prototyping to implementation.
Let's dive deep into the benefits of using AI code completion tools and how they are revolutionizing the software development process.
Why are AI Code Generators Getting Popular?
Driven by their ability to revolutionize the software development landscape, code generators have become extremely popular. The rise of generative artificial intelligence in recent years has further pushed developers to adopt AI code completion solutions to enhance productivity. Some of the key reasons for the rising popularity of AI-based code assistant systems include:
High-Quality Code: AI code completion solutions can generate repetitive code and provide suggestions based on already written code stored in an organization's code repositories. It also promotes consistency by making code readable and understandable for developers. These AI assistants provide syntax or sample examples to avoid coding errors. Such capabilities reduce the time to find new methods to implement optimized code while working on projects.
Expedite Software Delivery: AI assistants empower users with sample code using static code analysis to expedite software development. Developers can simply provide instructions in plain English language to obtain code snippets around software development tasks, including security- and networking-related code. To simplify the entire software development workflow, AI code generators also maintain company-wide consistency with naming conventions, formatting, and styling. This whole process saves time and repetitive efforts of both team and organization, leading to faster software delivery.
Benefits of AI Code Generator
Any software solution provider employs and maintains source code. For developers, this can mean performing multiple tasks daily. The most significant impact a code generator tool can bring is to help code faster with maximum optimization. They help developers focus on complex problems and take care of repetitive and mundane tasks.
Here are some benefits of auto-generated code for developers and businesses:
Creates Code Summarization in Simple Language: It provides a summarization of functions used in the code to help developers better understand code contributed by other team members to collaborate effectively.
Auto Completes a Code: Developers can either provide prompts for their requirements or start writing code for the AI tool to develop the desired code.
Generates Complex Database Queries: Automated code generators provide not only code suggestions but also query creation. It can build queries from scratch based on the prompts provided by the developer.
Supports Multiple Programming Languages: Programming task with AI has now become seamless as it supports multiple languages.
Provides Smooth Integration: With contextual code and a natural language interface, these tools integrate smoothly into the existing development workflow. Developers can integrate them with their preferred IDEs and GitHub repositories to simplify software development.
Reduced Costs: It enables developers to focus on complex software development tasks rather than implementing tedious, repetitive coding tasks. This allows companies to utilize their resources efficiently.
Top Five AI Code Generators
There are multiple paid and open-source automated code tools available in the market. We will dive deep into the top five AI-powered code completion tools that will help organizations choose the best one according to their requirements.
Developed by Salesforce, CodeT5 is an open-source pre-trained encoder-decoder model to produce codes automatically. One of the key differences of CodeT5 is that it uses machine learning (ML) and natural language processing (NLP) algorithms to analyze the requirement and generates the outcome based on pre-trained models. Most of the code tools provide direct code for generic use cases as they are encoder- or decoder-only models. However, CodeT5 understands the context to provide desired code for software development.
Tabnine is one of the best AI code tools available that specializes in whole-line and full-functionality code completion as it employs deep learning algorithms. However, it translates natural language to generate code. Including popular integrated development environments (IDEs ), Tabnine supports 15 editors and more than 20 programming languages.
Tabnine works best with teams. It uses machine learning to understand how team members work by analyzing patterns and preferences. Based on such understandings, it provides personalized code that aligns with the teams' workflows. This ensures that developers maintain consistency for better collaboration and rapid development. Tabnine empowers organizations with the flexibility to run Tabnine on the cloud or locally (self-hosted). Organizations can also centrally manage Tabnine to ensure data security and privacy; project managers can only allow Tabnine to be trained on specific repositories. Owing to its features, Tabnine now has grown into a complete AI code completion tool.
Today, it is available in three versions: Starter, Pro, and Enterprise. The starter plan is available for free and supports short completion of code. However, Pro and Enterprise have enhanced features, like they provide recommendations for code completion based on users coding patterns and styles.
GitHub Copilot is an AI-powered tool developed to automate and suggest code. Along with code suggestions, developers can use it to translate code from one programming language to another. The capabilities of GitHub Copilot have further improved with GitHub Copilot X, a GPT-4 powered AI-paired programmer. It can now explain code to provide developers with more context while collaborating with team members. Developers can also select code snippets and ask GitHub Copilot to find and fix the code.
GitHub Copilot is not only revolutionary in terms of code generation, but developers can also use it for creating pull requests to compare the changes and merge the pull request automatically. It is best suited for managing the entire project development cycle, as developers can use plain English language to write test cases. This testing ability makes software development more robust and reliable.
PolyCoder is one of the open-source AI code generator tools based on OpenAI's GPT-2 language model. Unlike other models, PolyCoder is trained to generate code from user comments, not natural language. PolyCoder is best known for outperforming other code-generation tools in the C-programming language. Therefore, it is popular among game developers and professionals who optimize for speed in their projects. As it is an open-source tool, PolyCoder is widely used by several developers.
Cogram is one of the intelligent code completion tools that aims at helping data scientists and Python programmers to perform code completion tasks using Jupyter Notebooks and SQL queries. It is specially designed for data scientists and data analysts. It allows data scientists to generate visualizations based on Python modules such as Plotly and Matplotlib. Cogram supports producing code from natural language understanding or comments and performs the code auto-completion using the tab key or double ##. This can also include generating complex SQL queries, especially for joins and grouping. Cogram is ideal for developers who write code in Python. It supports PostgreSQL, Amazon Redshift, MySQL, SQLite, and Amazon Redshift.
Limitations of Using AI Coding Tools
While the rise of generative AI is transforming industries and unlocking opportunities, it also welcomes new challenges. While these tools cut down development costs, it also increases security, privacy, and copyright issues. Here are the top three limitations of using them:
Not Suitable for Complex Projects that Require Highly Customized Code
Most of these tools generate code by analyzing existing code and patterns. This means it overcomes repetitive tasks, generates code faster, and provides optimized code. But when the project needs customized solutions, developers need new code development. Using an AI code generator on tasks that have never been done before with respect to their business niche can be challenging.
Industries must partially rely on software development automation. However, reviewers and developers must verify if the code meets the expected results. As the code generated by these tools is based on natural language queries, the results may not be directly related to your particular use case. In such cases, it might get difficult for developers to understand the code.
Results May be Inaccurate or Biased Due to Biased Training Data
Since AI generates code based on the datasets used for training, it could have potential bias. Most of the AI-powered tools are trained on GitHub repositories, which have over 250 repositories. These repositories are not monitored for the quality of code, or in most cases, the code might not be annotated in a way that is fit for training models. This could result in having code that might not be the best fit for your use case.
AI-powered code solutions are a new approach that challenges the traditional coding process. It has changed how tasks are performed by developers. On the other hand, it has also simplified the software development life cycle. However, with software solutions all around the corner, it is essential to expedite the development and delivery of solutions to gain a competitive advantage.
But considering the limitations, one cannot entirely rely on these solutions for every use case. It is crucial to understand the boundaries of what these coding tools can do and what they cannot. Nevertheless, these AI-based code generation tools are a revolutionary technology that is helping software engineers and developers build better applications.
Can AI solve coding problems?
Yes, AI advancement can now solve coding problems by reducing developers' repetitive work. It can provide suggestions for basic and complex code, which saves developers time.
What are the top AI code generators?
Some of the best code completion tools includes CodeT5, Tabnine, GitHub Copilot, PolyCoder, and Cogram. Depending on the use cases, developers can choose one over the other for a seamless development workflow.
Is it possible to code with AI tools?
Yes, AI tools are a powerful technique to enhance developers' productivity. Developers can quickly generate code with AI-assisted coding tools with simple English language prompts or get assistance in code completion.