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Published Oct 10, 2023 ⦁ 12 min read

Say Goodbye to Burnout: ChatGPT Tools to Automate Coding Grunt Work

Introduction

Developers today face increasing pressure to deliver more features at a faster pace than ever before. With tight deadlines and complex applications, it's easy to get burned out handling repetitive coding tasks like writing boilerplate code, documentation and tests. Fortunately, the rise of AI chatbots like ChatGPT provides some much needed relief for overwhelmed developers.

In this post, we'll explore how ChatGPT can help automate grunt work and increase productivity. You'll learn prompt examples and best practices for using ChatGPT to generate code, documentation and tests. We'll also discuss strategies to validate ChatGPT's work and integrate it into your workflows. By offloading rote tasks to ChatGPT, you can focus on big-picture thinking and avoid developer fatigue. Let's dive in!

ChatGPT's Promise for Developers

ChatGPT exploded in popularity thanks to its ability to understand natural language prompts and generate coherent responses. For developers, it can quickly produce everything from code snippets to detailed documentation. This frees up mental bandwidth previously spent on tedious tasks. Early adopters report massive time savings and improved job satisfaction. However, blindly implementing ChatGPT's suggestions can lead to mistakes. With responsible use, it empowers developers to do their best work.

Understanding ChatGPT

Before leveraging ChatGPT for developers, it helps to understand what it is and how it works.

ChatGPT is a conversational AI chatbot created by Anthropic, an AI safety startup. It uses a sophisticated natural language processing model called Claude trained on massive datasets scraped from the internet. Unlike rigid rule-based bots, Claude takes a statistical approach to generate human-like exchanges.

While very impressive, Claude has some key limitations. It does not actually understand language or possess general intelligence. Its knowledge cut off in 2021, so it lacks current events awareness. Claude can make convincing but incorrect statements, so human oversight is critical. However, for constrained tasks, it demonstrates creativity and reasoning that saves developers tremendous effort.

Claude's Relevance for Developers

For developers, Claude's language mastery makes it adept at coding-related tasks. It can translate descriptions of software requirements into working code. Its statistical foundations help Claude suggest sensible function names, parameters and language idioms. While it won't replace human judgment, Claude can bootstrap projects faster than ever.

Current Capabilities and Limitations

Claude cannot reason about novel situations outside its training data. However, it excels at text generation constrained by prompts. For developers, this makes Claude great for rote coding tasks but unreliable for high-level planning. Checking Claude's work is a must, but within well-defined domains, it supercharges productivity.

ChatGPT for Code Generation

One of the most direct applications of ChatGPT for developers is code generation. While it shouldn't replace writing core application logic, ChatGPT makes quick work of rote coding tasks.

For example, you can provide a natural language description of a function you need and ask ChatGPT to generate the full code in your language of choice.

Prompt: Write a Python function that accepts a string as input and returns a copy of the string with all vowels removed. Include type hints, docstrings, and return the modified string.

Response:

```python
def remove_vowels(text: str) -> str:
    """
    Removes all vowels from the input string and returns the result.
    
    Args:
        text (str): The input string
    
    Returns:
        str: Copy of input string with vowels removed
    """
    
    vowels = "aeiouAEIOU" 
    no_vowels = []
    
    for char in text:
        if char not in vowels:
            no_vowels.append(char)
            
    return "".join(no_vowels)

This saves the tedious effort of writing and testing all the code manually. You can also generate entire classes, JSON schemas, API routes, and more using detailed prompts.

While excellent for boilerplate code, ChatGPT may struggle with complex logic like enforcing business rules or optimizing performance. Combining human ingenuity for core algorithms with ChatGPT's coding grunt work provides the best results.

Pros vs Cons of Automated Code Generation

Automatically generating code via ChatGPT has many benefits:

  • Huge time savings on repetitive coding tasks
  • Allows focus on complex logic and architecture
  • Code generated follows best practices and conventions
  • Easy to get starter code up and running quickly

However, there are also some downsides to consider:

  • Can introduce difficult to detect bugs if not thoroughly reviewed
  • Lacks deep understanding to handle complex logic
  • Code may not integrate well with existing architecture
  • Overreliance can cause developer skills erosion

Overall, leveraging ChatGPT for simple repetitive tasks while manually writing core logic appears the ideal balance.

Comparison to GitHub Copilot

GitHub Copilot is another AI pair programmer that suggests code completions in real-time. Copilot stays narrowly focused on the immediate context to make relevant recommendations. Meanwhile, ChatGPT takes a higher level approach to generate entire functions or classes based on natural language prompts. Copilot's inline suggestions can complement ChatGPT's broader code generation capabilities.

Prompting Best Practices

Here are some tips for prompting ChatGPT to generate cleaner code:

  • Use clear, jargon-free explanations of what the code should do
  • Specify important parameters, data types, and return values
  • Provide examples of expected input and output
  • Ask for readable variable names and comments
  • Request idiomatic code that follows language standards
  • Require inclusion of error handling, boundary checks, etc.

With well-structured prompts, ChatGPT can produce high quality starter code to accelerate development. But always thoroughly review the results before implementation.

ChatGPT for Documentation

ChatGPT also excels at producing documentation to explain code concepts and save developers hours of writing.

For example, you can provide an API specification and ask ChatGPT to generate a getting started tutorial:

Prompt: Write a beginner friendly tutorial for how to use the RandomUser API https://randomuser.me/documentation. Explain what the API does, how to make requests, parameters to include, and provide examples for fetching and displaying user profile data. Use headings, code blocks, images, and a friendly conversational tone.

Response:

# Getting Started with the RandomUser API

The RandomUser API makes it easy to generate random user data for your applications. In this beginner's tutorial, we'll cover everything you need to make your first request and display user profiles.

## Overview of the RandomUser API

The RandomUser API returns a JSON object containing a randomly generated user profile each time you make a request. The profiles contain details like name, email, address, birthdate, and more.

It's a great way to quickly add dummy user data to populate prototypes and demo apps during development.

## Making Your First Request

To fetch a single random user, make a GET request to this endpoint:

https://randomuser.me/api/


Try it out in your browser or code editor - you should see a JSON response containing profile information.

To get multiple user profiles in a single request, add the "results" parameter and specify the number of users to generate:

https://randomuser.me/api/?results=5


This returns 5 complete user objects in the response. Easy!

## Displaying Profile Data

Now let's display some of the user profile data in our app. Here's an example fetching and showing the profile image, name, and email:

```js
// Fetch single user data
fetch('https://randomuser.me/api/')
  .then(response => response.json())
  .then(data => {

    // Display properties
    const user = data.results[0];
    
    document.getElementById('image').src = user.picture.large;
    document.getElementById('name').textContent = `${user.name.first} ${user.name.last}`; 
    document.getElementById('email').textContent = user.email;

  });

The full docs explain all the profile properties available.

And that covers the basics of using the RandomUser API! With just a few lines of code, you can start adding randomized test data to your apps.


This provides a great starting point that can be refined as needed, saving hours of effort explaining and demonstrating API usage.

In addition to tutorials, ChatGPT can generate:

- API documentation
- Software documentation
- Code comments
- Wiki pages/FAQs
- Changelogs

For any well-defined doc need, ChatGPT can produce high-quality initial drafts for review.

### ChatGPT vs Manual Documentation

While ChatGPT speeds up documentation drafting, manually writing docs has advantages:

- Human touch provides clearer explanation
- Can tailor to specific audience needs
- Allows documenting before/while coding 
- Facilitates deeper understanding of code

Using ChatGPT and manual documentation together combines the best of both worlds.

### Keeping Documentation Updated

A downside of generated documentation is keeping it updated as code evolves. Strategies like:

- Documenting components in isolation
- Using semantic identifiers
- Adding version info 
- Storing prompts alongside docs

Can help minimize rework when regenerating with ChatGPT.

### Prompt Formatting Tips

Some best practices for prompting ChatGPT for docs:

- Use simple, direct statements of what you want
- Provide lots of relevant context and examples  
- Ask for specific doc elements like summaries, diagrams, code blocks
- Specify a target audience and tone

With practice, you can fine-tune prompts to produce excellent starting docs.

## ChatGPT for Testing

In addition to generating code and docs, ChatGPT can also help develop test cases to validate functionality and prevent regressions.

For example, you can describe a function and ask ChatGPT to provide unit test coverage:

Prompt: Write 5 unit test cases using the Python unittest framework to thoroughly test this function:

def word_count(text):
  words = text.split(" ")
  return len(words)

The test cases should validate both normal behavior and edge cases.

Response:

import unittest

class TestWordCount(unittest.TestCase):

    def test_simple_sentence(self):
        text = "This is a simple sentence"
        expected = 4
        actual = word_count(text)
        self.assertEqual(actual, expected)

    def test_empty_string(self):
        text = "" 
        expected = 0
        actual = word_count(text)
        self.assertEqual(actual, expected)

    def test_punctuation(self):
        text = "Here's a sentence, with some punctuation!"
        expected = 7
        actual = word_count(text)
        self.assertEqual(actual, expected)
    
    def test_multiple_spaces(self):
        text = "This  sentence  has extra   spaces"
        expected = 5
        actual = word_count(text)
        self.assertEqual(actual, expected)

    def test_tabs_and_newlines(self):
        text = "This\nsentence\n\thas\ttabs\nand\nnewlines"
        expected = 6
        actual = word_count(text)
        self.assertEqual(actual, expected)

This provides a starting set of test cases that can reveal bugs missed during initial development.

Pros and Cons of Automated Test Generation

Automatically generating tests with ChatGPT has advantages like:

  • Quickly creates initial coverage
  • Considers edge cases developers may miss
  • Saves time over manual test writing

However, potential issues include:

  • Could overlook application-specific failure modes
  • Lacks a deep understanding of code logic
  • Tests may be too simplistic or formulaic
  • Review still required to identify gaps

The best results come from using ChatGPT tests to supplement those written manually.

Maximizing Test Coverage

Here are some tips to get thorough test cases from ChatGPT:

  • Provide lots of context on expected behavior
  • Include edge case examples
  • Specify types of tests needed (unit, integration, fuzzing)
  • Ask for tests validating both success and failure modes
  • Request at least 5-10 test cases per function

With well-formed prompts, ChatGPT can automate much of the drudgery of test writing.

Other Developer Uses

In addition to the major applications covered above, ChatGPT also provides some less mainstream but still impactful benefits for developers:

  • Bug Fixing - Describe an errant behavior and ask ChatGPT to suggest potential patches.
  • Code Review - Paste a code snippet and have ChatGPT analyze and improve it.
  • Prototype Ideas - Instantly mock up anticipated functionality to test concepts.
  • Refactoring - Explain complex components and ask to simplify the architecture.

As the technology progresses, more advanced capabilities will likely emerge like:

  • Analyzing performance bottlenecks
  • Providing security guidance
  • Conducting dependency analysis
  • Identifying outdated packages/libraries

Integrations with developer tools and IDEs will also help incorporate ChatGPT into existing workflows. The future looks bright for AI assistance!

Maximizing ChatGPT's Potential

While ChatGPT can generate helpful code, docs and tests, following some best practices will get the most mileage out of the technology:

Crafting Effective Prompts

  • Use simple, plain language to describe desired outcomes
  • Include plenty of relevant context and examples
  • Ask for specifics like function arguments, code style, doc sections
  • Limit prompts to well-defined tasks within ChatGPT's capabilities

Validating the Output

  • Review code for bugs, unclear logic, or style inconsistencies
  • Verify docs for accuracy, clarity, and correct terminology
  • Thoroughly test generated code with provided test cases
  • Compare ChatGPT's suggestions to alternate solutions

Iteratively Improving Responses

  • Use ChatGPT's feedback feature to correct mistakes
  • Refine prompts based on its initial attempts
  • Ask follow-up questions to fill in gaps
  • Iterate until satisfied with results

Responsible Integration

  • Avoid fully automating end-to-end workflows initially
  • Handle core logic manually, outsource rote tasks to ChatGPT
  • Phase in adoption while evaluating impact on productivity/quality

Carefully applying these principles allows smoothly integrating ChatGPT's substantial capabilities into developer workflows.

Conclusion

ChatGPT provides transformative AI assistance to help developers save time on repetitive coding tasks. With careful prompting, it can generate boilerplate code, documentation and tests to accelerate development and prevent burnout. While ChatGPT cannot fully replace human creativity and judgment, responsibly integrating its output into workflows provides massive leverage for developers stretched thin.

By understanding ChatGPT's strengths and thoughtfully validating its results, developers can reclaim hours previously lost to drudgery. Instead of endlessly grinding through monotonous tickets, they can focus on big picture strategy and innovation. With AI picking up the tedious tasks, developers can maximize their value and enjoyment of bringing ideas to life.

To see for yourself how ChatGPT can aid your workflow, visit DevHunt. Their platform showcases developer tools powered by AI like ChatGPT that are reinventing coding. Harnessing this technology responsibly promises more productive and fulfilling work for developers. The future of AI looks bright, and you can help shape it.

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