But! Copilot may not be a one-size-fits-all solution. Different developers have different preferences, workflows, and needs. Recognizing this diversity, let’s explore alternative tools that offer similar code completion features (and more). 

Quick Intro to Useful Alternative Plugins to GitHub Copilot 

We’ll delve into three notable alternatives of GitHub Copilot: Tabnine, AWS Codewhisperer, and Bito.  

Before diving into the distinctive features of each tool, here’s a quick look at what each one brings to the table: 

Tabnine 

Tabnine was an early player in terms of code completion tools, and it provides a practical plugin compatible with your preferred code editor. It offers suggestions for whole lines of code and full-function completions across various languages, libraries, and frameworks. And it does so based on natural-language comments. 

One noteworthy aspect of Tabnine is its efficiency with minimal context compared to Copilot. It suggests code midline as you type, without waiting for one line to be finished before moving to the next.  

For teams and organizations, Tabnine offers the capability to host and train their own AI models. This feature facilitates collaborative autocompletion across IDEs, which enhances teamwork and contributes to code security. 

Beyond code completion, Tabnine tackles unit test support. Using advanced AI, it automatically generates unit tests for your code, a helpful feature for ensuring thorough testing and code stability, especially on larger projects. 

The interface is user-friendly, requiring minimal configuration. As you use it more, Tabnine adapts and tailors unit test suggestions to your coding style.  

AWS CodeWhisperer 

Amazon CodeWhisperer understands natural language comments in English and offers suggestions ranging from snippets to entire functions—up to 10–15 lines—in various IDEs. Similar to Tabnine, CodeWhisperer adapts to your coding style and ensures a personalized coding experience. It also extends its capabilities to the command line with inline documentation and AI-driven natural-language-to-code translation.  

For those leveraging AWS services, CodeWhisperer analyzes code in real time, with suggestions tailored to AWS APIs like Amazon EC2 and AWS Lambda and aligned with best practices for cloud-based projects. 

To ensure responsible coding, the tool introduces a reference tracker for open-source code. This feature flags suggestions resembling public code with detailed annotations for review. The goal here is to actively avoid biases in code suggestions. 

If you’re choosing between AWS CodeWhisperer and Tabnine, consider what you prioritize. Let’s say you value versatility and broad language support across various editors; then Tabnine is your pick. CodeWhisperer, on the other hand, is for those who focus on precision — the tool learns from in-house code and tailors suggestions to an organization’s practices. 

Bito 

Bito allows users to generate code in any language, create comprehensive test cases, and gain insights into selected code snippets with the Explain Code feature. For documentation, Bito offers a Comment Method tool, while the Improve Performance feature provides tips for enhancing code efficiency. Security concerns are addressed through the Check Security function, which lets you inquire about potential issues.  

What sets Bito apart from GitHub Copilot is its integration with Visual Studio Code — it leverages embeddings and a vector database to understand your local codebase. This ensures context-aware suggestions, a distinctive advantage over Copilot. 

Another noteworthy feature is the Generate Unit Test Shortcut:  

The shortcut allows developers to efficiently generate test cases for different code paths and scenarios. This ensures comprehensive testing coverage and reduces the risk of undetected bugs.  

The consistent testing standards promoted by Bito foster collaboration and codebase maintainability within development teams, as all members can adhere to the same testing practices.  

On Ethics for AI Code Completion Plugins in Software Development 

Respecting intellectual property rights and privacy is crucial for ethical software development, fostering trust in the industry.

Developers using code completion tools like Tabnine, Bito, etc. should: 

• ensure that sensitive or confidential data is not intentionally or unintentionally exposed, 
• be mindful not to breach non-disclosure agreements (NDAs), 
• seek explicit consent from clients or stakeholders before employing coding assistant tools in projects involving their codebases. 

On the level of software development organizations, it’s a best practice to regulate AI tools usage in processes. Many responsible engineering companies already create and implement AI tools usage policies for employees on how to be safe and secure when using generative AI, especially when it involves the sharing of potentially sensitive company and customer information. 

Summing Up

The implementation of AI assistants like Tabnine, AWS CodeWhisperer, and Bito has resulted in tangible time savings and enhanced productivity. And we have the numbers to back this up. 

In just two weeks, Tabnine contributed to a nearly 13-hour timesaving for an Intetics engineer. That month, 27% of that engineer’s code was produced by the tool.  

With AWS CodeWhisperer, the official figures claim a 57% acceleration in developer productivity and a 27% increase in the likelihood of project success.  

As for Bito, the subjective experience reveals improvements in speed, particularly in implementing new features. The initial estimate suggested a potential 27% code contribution, but real-world results prove a more conservative productivity boost of 10-20%. Also, Bito offers remarkable efficiency, potentially saving up to 80% of time spent on writing unit tests.  

To wrap it up, embrace the tools that empower you to code better and faster. 

Featured materials: 

• ChatGPT for Software Developers: Better Code, Increased Productivity, and Premier Product Quality [With Examples of Prompts]
• Why GitHub Copilot Is the Ultimate Game-Changer for Software Developers
• AI-Augmented Project Management: 15 Ways to Automate Time-Consuming Tasks with ChatGPT [+12 Other AI Assistants]

The post Alternatives to GitHub Copilot: Tabnine, AWS CodeWhisperer, and Bito to Increase Developers’ Productivity by 20% appeared first on Intetics.

Let’s break it down and see why it’s a handy tool for programmers to speed up the coding process, learn new frameworks or libraries, or explore different ways of solving problems.  

What Is GitHub Copilot? 

GitHub Copilot is an AI pair programmer providing auto-complete style recommendations as you code. It accelerates your coding process by analyzing contextual elements like variable names, surrounding code, and function signatures, generating relevant suggestions in real time.  

The concept of an “AI pair programmer” underscores its collaborative nature, emphasizing that it complements and augments your coding efforts—it cannot work without you. 

GitHub Copilot is powered by a generative AI model developed by GitHub, OpenAI, and Microsoft. 

Once installed, the coding assistant analyzes the developer’s code, providing suggestions for completing code snippets, suggesting relevant functions, and even refactoring existing code. 

Read also: ChatGPT for Software Developers: Better Code, Increased Productivity, and Premier Product Quality [With Examples of Prompts]

Installing GitHub Copilot: Step-by-Step Guide 

GitHub Copilot integrates with various popular integrated development environments (IDEs), such as Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs.  

GitHub Copilot can be accessed through personal accounts with GitHub Copilot Individual or organizational accounts with GitHub Copilot Business. GitHub Copilot is free for verified students, teachers, and maintainers of popular open-source projects. 

You can try GitHub Copilot for free with a one-time 30-day trial. After the free trial, you will need a paid subscription for continued use. Billing plans are available at GitHub Docs. 

To get started, you’ll need the following: 

• An active GitHub Copilot subscription; 
• Microsoft Visual Studio Code (or another preferred IDE); 
• The GitHub Copilot extension installed in the Visual Studio Code. 

How to Install GitHub Copilot? 

• Find the GitHub Copilot extension page in the Visual Studio Code Marketplace and click Install. 
• Click Open Visual Studio Code in the pop-up window that appeared.  
• Click Install in the “Extension: GitHub Copilot” tab in Visual Studio Code.  
  • If you have not previously authorized Visual Studio Code in your GitHub account, you will be prompted to sign into GitHub in Visual Studio Code.  
  • If you have previously authorized Visual Studio Code for your account on GitHub, GitHub Copilot will be automatically authorized.  
• If you don’t get the prompt to authorize, click the bell icon in the bottom panel of the Visual Studio Code window. 

• In your browser, GitHub will request the necessary permissions for GitHub Copilot. To approve these permissions, click Authorize Visual Studio Code.  
• To confirm the authentication, in Visual Studio Code, in the “Visual Studio Code” dialog box, click Open. 

GitHub Copilot can be customized to align with your coding style. Configure settings like indentation, line length limits, and suggestion verbosity to make Copilot an extension of your coding preferences. 

Creation of UI Elements with Prompts in GitHub Copilot 

GitHub Copilot’s capability to generate code suggestions extends to creating user interface (UI) elements. By providing clear and concise prompts, developers can elicit relevant code snippets for various UI components, such as buttons, dropdown menus, and text fields.  

Example: We need to create a tracker of monthly income and expenses.

Let’s generate a prompt for GitHub Copilot: 

<!–bootstrap grid with 12 rows and 1 column for small screens, and 6 rows and 2 columns for medium screens and above, each cell contains label of the month from January to December, and 2 bootstrap number inputs side by side with labels, 1 for income and 1 for expenses –>) 

Based on the input, GitHub Copilot starts to suggest the code to generate the desired output. 

To accept the suggested code, just press Tab, and the assistant will write the code. 

It’s crucial to understand that Copilot isn’t flawless enough to write code independently, as we highlighted earlier, its “pair programmer” nature. You are responsible for crafting the overall logic, guiding the tool with well-thought-out prompts, and overseeing the suggestions it generates. You steer the process through practical and precise instructions while the tool accelerates the code-writing process for you.  

So, after a brief review, the developer reviews and improves the proposed structure and creates the desired table faster. 

GitHub Copilot proves to be a valuable tool for frontend developers to elicit relevant code snippets for various UI components efficiently. The example illustrates the importance of reviewing Copilot’s suggestions by an experienced developer to ensure correctness and code optimization.   

While Copilot accelerates the code-writing process, user intervention remains essential for ensuring logic, accuracy, and adherence to specific requirements. Let’s focus on how to engineer prompts that guarantee Copilot’s efficient assistance. 

Getting Desired Outcomes with Prompts: Best Practices 

Once we have reviewed the capabilities to generate entire functions, classes, or blocks of code faster, let’s touch on the best practices for prompt engineering to give clear instructions to GitHub Copilot and generate desired results. 

• High-level context in comments: Provide high-level context in a comment at the top of the file and leave more detailed instructions in the form of comments and code. 
• Provide specific details: For precise code suggestions from GitHub Copilot, provide specific details. If you aim, for example, to fetch data from an API, specify the type of data, processing method, and the target API endpoint. 
• Provide examples: Apart from instructing GitHub Copilot verbally, you can also demonstrate desired actions using examples in your preferred coding style.  
• Iterate and refine: Continuously refine your code with Copilot’s suggestions. Start with its recommendations and customize them to meet your specific requirements. Regularly review and adjust the generated code to match your coding style and project needs. 
• Validate generated code: Validate Copilot-generated code for correctness, efficiency, and adherence to standards. Conduct thorough code reviews, run comprehensive tests, and make necessary adjustments to maintain codebase quality. 

In turn, GitHub is experimenting with LLMs to evolve Copilot, focusing on creating a developer-friendly AI experience that is predictable, tolerable, steerable, and verifiable to enhance overall productivity and effectiveness. All this is done to extend GitHub Copilot across the developer lifecycle. 

Other Ways to Enhance Your Coding Workflow with GitHub Copilot 

Explore additional capabilities of GitHub Copilot to refine your coding process:  

• Code refactoring: GitHub Copilot aids in refactoring by suggesting concise and efficient alternatives, identifying redundancies, simplifying complexity, and recommending improved coding patterns for cleaner, more maintainable code.  
• Error & exception handling: Copilot assists in error handling by suggesting mechanisms and blocks for exceptions, enhancing code robustness and resilience to unexpected scenarios.  
• Customization: Enhance GitHub Copilot by customizing its training with your codebases, allowing it to generate more accurate, context-aware suggestions tailored to your specific application domain.  
• Consistency across teams: GitHub Copilot is valuable for team development, aiding in maintaining coding standards by suggesting consistent styles and practices. Its suggestions are discussion starters in code reviews, fostering collaboration, and knowledge sharing among team members. 

Summing Up 

It still requires profound expertise to build software. Even with Copilot’s help, a developer should verify and understand generated code. Non-programmers can’t jump on Copilot and make whatever they want. Or is it so yet?   

Keep a keen eye on the dynamic AI assistant’s landscape—experiment with emerging options in your environment to identify the ones that align best with your requirements. Explore tools like GitHub Copilot, and stay tuned to learn more about alternatives: Bito, Amazon CodeWhisperer, Tabnine, etc.  

Join us to empower developers to be more productive at every stage of the software development lifecycle. 

The post Why GitHub Copilot Is the Ultimate Game-Changer for Software Developers appeared first on Intetics.