My Experience with GitHub Copilot: A Year of Boosted Productivity

I'm Sachidanand Sharma, an Integrated Solution Lead (ISL) with a passion for GenAI and Sustainability. I have 20 years of experience in the software industry, wearing different hats as a Software Engineer, Lead, Architect, and Cloud/IoT Solution Architect. I enjoy working on challenging projects that involve cutting-edge technologies and innovative solutions. That's why I was thrilled to discover GitHub Copilot, an AI pair programmer that has transformed my coding experience since I started using it last year.

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Introduction

For programmers, time is a precious commodity. We spend countless hours wrestling with code, debugging errors, and searching for the most efficient solutions. Enter GitHub Copilot, an AI pair programmer that has revolutionized my workflow since I began using it last year. In this article, I'll delve into my experience with Copilot, exploring its impact on my productivity, creativity, and overall coding experience.

A Powerful Productivity Booster

The most immediate benefit of Copilot is the undeniable boost it provides to my coding speed. Repetitive tasks like writing boilerplate code, generating getters and setters, and implementing common data structures are now handled effortlessly by Copilot's suggestions. This frees up valuable mental space and allows me to focus on the more challenging and creative aspects of programming.

Beyond basic code completion, Copilot excels at understanding the context of my projects. It suggests relevant functions, variable names, and code structures based on the surrounding code and comments. This contextual awareness has significantly reduced the time I spend searching for solutions online, allowing me to stay in the flow state and maintain my focus.

Enhancing Creativity and Problem-Solving

While Copilot automates some tasks, it doesn't diminish the need for creativity and problem-solving skills. Instead, it acts as a powerful brainstorming tool. When I encounter a challenging coding problem, Copilot often suggests unexpected approaches or alternative solutions I might not have considered on my own. This sparks new ideas and helps me approach problems from different angles, leading to more efficient and elegant solutions.

Beyond Code Completion: A Collaborative Partner

Copilot's capabilities extend beyond simply suggesting code. It can also generate comprehensive docstrings, translate code comments into different languages, and even write unit tests. This additional functionality makes Copilot a true collaborative partner, assisting me throughout the entire development process.

Not a Silver Bullet

Despite its remarkable capabilities, it's important to acknowledge that GitHub Copilot is not a silver bullet. The suggested code requires review and understanding before blindly implementing it. Additionally, it can sometimes generate incorrect or suboptimal solutions, so a critical eye is always necessary.

My Personal Experience

Now that I've outlined the general benefits of Copilot, I want to share some specific examples of how it has impacted my workflow.

Example-1:

One of the most significant ways Copilot has boosted my development is by facilitating my transition into a new programming language. As an Azure Cloud Architect and .Net developer for many years, I recently decided to expand my skillset by learning Python. While I had a solid foundation in programming concepts, mastering a new syntax and libraries could have been a time-consuming endeavor.

This is where GitHub Copilot truly shined. By leveraging its code completion and contextual suggestion features, I was able to learn Python at an accelerated pace. Copilot's ability to understand my intent and suggest relevant code snippets in Python significantly reduced the time I spent memorizing syntax and searching for solutions online.

For example, while building a Generative AI (GenAI) solution using Python, I encountered challenges with specific libraries and functionalities. Copilot, by analyzing my code and comments, would suggest relevant function calls, class implementations, and even best practices for utilizing those libraries. This not only accelerated the development process but also deepened my understanding of Python's capabilities within the context of GenAI development.

Within a month of actively using Copilot alongside dedicated learning resources, I was able to develop mature GenAI solutions using Python. This experience solidified my belief that Copilot is a powerful tool not just for experienced developers but also for those venturing into new programming territories.

Example-2:

Beyond facilitating my transition to Python, Copilot has also empowered me to delve into the fascinating realm of Large Language Models (LLMs). While customizing a complex LLM/SLM like PHi-3 might seem like a daunting task, Copilot proved to be an invaluable asset in this endeavor.

Traditionally, customizing LLMs/SLMs requires a deep understanding of the underlying architecture and the ability to write complex code for training and fine-tuning. However, with Copilot's assistance, I was able to navigate this process more efficiently.

For instance, Copilot played a crucial role in:

·?????? Understanding complex LLM/SLM libraries: When working with libraries specifically designed for LLM training and fine-tuning, Copilot helped me decipher their functionalities by suggesting relevant code examples and documentation.

·?????? Data Preprocessing and Augmentation: The process of preparing custom data for LLM training often involves complex data cleaning and augmentation techniques. Copilot's ability to suggest code snippets for data manipulation and augmentation tasks significantly streamlined this process.

·?????? Fine-tuning the LLM/SLM: Fine-tuning an LLM/SLM with custom data requires meticulous adjustments to training parameters. Copilot's suggestions for hyperparameter optimization techniques proved valuable in achieving optimal results.

?Example-3:

Beyond accelerating development and learning new languages, Copilot has also transformed the way I approach business idea validation. Traditionally, validating new ideas can be a time-consuming and resource-intensive process. However, Copilot has streamlined this process by enabling me to rapidly develop Proof-of-Concept (POC) prototypes.

Here's how Copilot aids in idea validation:

·?????? Rapid POC Development: Instead of spending weeks coding a complex prototype, Copilot allows me to quickly build a basic POC using its code completion and suggestion features. This lets me focus on the core functionality of the idea and validate its feasibility.

·?????? Data Validation: Copilot can assist in manipulating and analyzing sample data to assess the value proposition of the idea. For instance, it can help write code to clean and prepare data, perform calculations, and generate visualizations that provide insights into the potential impact of the idea.

·?????? Identifying Business Value: By analyzing the results of the POC and sample data analysis, I can quickly determine if the idea has the potential to generate business value. This allows me to prioritize promising ideas for further customer validation and resource allocation.

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Example-4:

?One of the most remarkable aspects of Copilot is its uncanny ability to adapt to my coding style and project environment. Unlike traditional code completion tools that offer generic suggestions, Copilot takes the following factors into account:

·?????? Coding Style: Copilot analyzes my previous code within the project and suggests completions that adhere to my preferred coding conventions (e.g., indentation, variable naming). This consistency improves code readability and maintainability.

·?????? Project Ecosystem and Environment: Copilot considers the specific libraries, frameworks, and dependencies used within my project. This allows it to suggest code snippets that are compatible with the existing environment and coding practices.

·?????? Supporting Files: Copilot doesn't operate in isolation. It factors in the contents of other files within the project solution, such as configuration files, class definitions, and documentation. This comprehensive understanding of the project context enables Copilot to provide highly relevant and accurate suggestions.

Unlocking Copilot's Potential: Best Practices

While Copilot offers a powerful set of features, getting the most out of it requires a specific approach. Here are some key practices I've incorporated to maximize Copilot's effectiveness in my workflow:

• Start Small, Aim Big: Instead of asking Copilot to generate entire functions or modules right away, begin with smaller prompts. Focus on specific code snippets like completing a loop or suggesting a relevant library function. Once you're comfortable with smaller suggestions, gradually increase the complexity of your prompts.
• Clarity is Key: Copilot thrives on clear and concise prompts. The more specific you are about your desired outcome, the better Copilot can understand your intent and provide relevant suggestions. Avoid vague prompts like "write a function for X" and instead provide details about the function's purpose, parameters, and expected behavior.
• Context is King: Don't treat Copilot as a code generation machine in isolation. Provide context by including relevant code snippets, function definitions, and comments from your project. This background information allows Copilot to understand the surrounding code and suggest solutions that seamlessly integrate into your existing codebase.
• Expected Outcome Matters: Whenever possible, specify the desired output of the code you're requesting. This helps Copilot tailor its suggestions to achieve the specific results you're aiming for.
• Test Incrementally: Resist the urge to write a large chunk of code based on Copilot's suggestions and then test it all at once. Break down your development process into smaller, testable units. Write a few lines of code with Copilot's assistance, test them thoroughly, and then move on to the next section. This incremental approach minimizes errors and ensures your code functions as intended.

Room for Growth: Addressing GitHub Copilot's Limitations

While Copilot is a powerful tool, it's essential to acknowledge its limitations. Here are some areas where I see potential for improvement:

• Focus on Delta Changes: Currently, Copilot sometimes generates entire code sections when prompted for additions to existing code. Ideally, it should focus on suggesting only the necessary changes (delta code) needed to achieve the desired functionality. This would streamline the integration process and minimize unnecessary code modifications.
• Contextual Consistency: In certain situations, Copilot can struggle with requests for code that needs to seamlessly integrate with existing code structures. This can lead to generic suggestions that don't align with the surrounding code. Improvements in Copilot's ability to understand and maintain code context would be beneficial.
• Change Tracking and Preservation: When modifications are made to the codebase, Copilot sometimes removes existing variable values or code snippets during subsequent suggestions. This can be disruptive and force developers to track down and re-implement their changes. Ideally, Copilot should intelligently integrate suggestions while preserving existing code and variable values whenever possible.

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In conclusion, GitHub Copilot has become an indispensable tool in my development workflow. It has demonstrably improved my productivity by automating repetitive tasks, facilitating rapid learning of new languages, and empowering me to explore the frontiers of AI development. Beyond its technical capabilities, Copilot fosters a more creative and efficient development process by enabling me to experiment with ideas, validate their feasibility, and focus on the core challenges.

However, Copilot is not without limitations. The need to carefully review suggestions, potential context-related inconsistencies, and occasional disruption to existing code require a critical and attentive approach. Despite these limitations, I believe Copilot represents a significant leap forward in developer productivity and collaboration. As the technology continues to evolve and address current shortcomings, I'm confident that GitHub Copilot will further revolutionize the way we approach software development.

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