Discover the transformative impact of ChatGPT on software development through real-life examples shared by industry experts. This article delves into the practical benefits of language models, from generating repetitive code to predicting edge cases. Gain invaluable insights as seasoned developers highlight ChatGPT’s role in streamlining coding processes and enhancing productivity.
- ChatGPT Builds Intuitive Data Models
- LLMs Generate Low-Risk Repetitive Code
- ChatGPT Assists in Boilerplate Code Generation
- ChatGPT Predicts Overlooked Edge Cases
- GPT Automates Code Documentation and Test Cases
ChatGPT Builds Intuitive Data Models
As a no-code/low-code software provider, we saw ChatGPT as a real threat when it first came out. However, after we discovered that it could build intuitive, industry-specific data models in seconds, our attitudes changed instantly.
A prompt like, “Build me a simple CRM data model using Salesforce Standard Objects,” delivers the following (abbreviated) output:
CRM Data Model Design
1. Account (Parent Object)
Represents a company or organisation. Can be a customer, partner, or prospect.
Key Fields:
Account Name (Text) Account Number (Auto-Number) Industry (Dropdown)
Type (Customer, Partner, Prospect, Vendor, etc.) Phone (Phone)
Billing Address and Shipping Address (Address) Website (URL)
2. Contact (Child of Account)
Represents individuals working at a company (Account). A Contact must be linked to an Account.
Key Fields:
First Name (Text) Last Name (Text)
Title (Text) Email (Email)
Phone (Phone) Account ID (Lookup to Account)
Mobile Phone (Phone)
3. Opportunity (Child of Account)
Represents potential sales deals. Linked to an Account (company) and optionally to a Contact.
Key Fields:
Opportunity Name (Text) Account ID (Lookup to Account)
Close Date (Date) Stage (Dropdown: e.g., Prospecting, Negotiation, Closed Won, Closed Lost)
Amount (Currency) Probability (%) (Formula or Manual Input)
Owner (Lookup to User)
The same approach can be applied to any sector imaginable. In fact, the more specialised the prompt request (e.g., “Build me a data model to manage gene splicing in mRNA vaccine development”), the more ChatGPT begins to show off the power of its underlying GPT Large Language Model.
Niall McCarthy, CEO, Aire No-Code
LLMs Generate Low-Risk Repetitive Code
I would qualify my response by saying that LLMs can greatly enhance the development process, and we have seen huge productivity gains utilising LLMs to generate low-risk, repetitive code—for example, with unit test creation, or general scaffolding for services and APIs. However, the toolset may be an IDE plugin versus switching to a ChatGPT-specific prompt, and regardless of the LLM, much care should be taken to avoid potential security, code leakage or copyleft issues. In short, ChatGPT itself may not be the interface, but the paradigm, if secure and compliant, really extends developer capability and productivity.
Lucas Hendrich, CTO, Forte Group
ChatGPT Assists in Boilerplate Code Generation
ChatGPT can enhance the development process by serving as an intelligent coding assistant for prototyping and problem-solving. One unique way I’ve utilised ChatGPT is for automatically generating boilerplate code and streamlining repetitive development tasks, saving hours of manual effort.
API Integration Example:
When integrating a RESTful API into a Python application, I needed boilerplate code to handle GET and POST requests while managing authentication headers and error handling. Instead of starting from scratch, I prompted ChatGPT to generate the initial setup:
“Generate a Python script to interact with a REST API. Include GET and POST methods, token-based authentication, and error handling.”
Output:
import requests
class APIClient:
def __init__(self, base_url, token):
self.base_url = base_url
self.headers = {
“Authorization”: f”Bearer {token}”,
“Content-Type”: “application/json”
}
def get(self, endpoint, params=None):
try:
response = requests.get(f”{self.base_url}/{endpoint}”, headers=self.headers, params=params)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
print(f”GET request failed: {e}”)
return None
def post(self, endpoint, data=None):
try:
response = requests.post(f”{self.base_url}/{endpoint}”, headers=self.headers, json=data)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
print(f”POST request failed: {e}”)
return None
# Example usage
if __name__ == “__main__”:
client = APIClient(base_url=”https://api.example.com”, token=”your_api_token”)
result = client.get(“example_endpoint”, {“key”: “value”})
print(result)
How It Helped:
- Speed: The boilerplate provided a solid foundation, allowing me to focus on the API’s specific logic rather than setup.
- Error Handling: Built-in exception handling saved time debugging edge cases.
- Customisation: I quickly tailored the script to match the API’s specific requirements.
This approach saved approximately 2-3 hours of manual setup and debugging, accelerating the project’s timeline. ChatGPT’s ability to provide quick, functional starting points improves developer efficiency and allows teams to concentrate on solving higher-level challenges.
Matthew Lam, Full-Stack Developer, Penfriend
ChatGPT Predicts Overlooked Edge Cases
As a founder deeply involved in both product development and engineering, I’ve found that one of the most impactful and unique uses of ChatGPT is accelerating quality assurance (QA) testing by predicting overlooked edge cases.
While ChatGPT is often used for generating code, its real value can come from acting as a creative “QA co-pilot”—identifying edge cases that developers or automated tools might miss. It can simulate a human tester’s mindset while simultaneously analyzing code.
Here’s a real-life example:
We had a feature that allowed users to upload files for audiobook conversion. I fed ChatGPT a description of the functionality and asked: “What are possible edge cases we should test for?” ChatGPT returned a goldmine of scenarios:
- Unusual file types: What happens if a user uploads an unsupported or corrupted file?
- Filename edge cases: How does the app handle long filenames or special characters?
- Concurrent uploads: Does the app handle two large files uploaded simultaneously?
- Progress interruptions: What happens if a user refreshes or loses connection mid-upload?
To take this further, I shared our upload code snippet and asked ChatGPT to analyze potential failure points. It suggested a few optimisations, such as adding validation for file integrity and fallback mechanisms for interrupted uploads.
Why this matters:
- Studies show that 70% of bugs are discovered late in testing or post-deployment, leading to delays and higher costs.
- With ChatGPT’s ability to “brainstorm” test scenarios and critique code, we reduced QA time by 30% for this feature and resolved potential issues before they could impact users.
By integrating ChatGPT into the development pipeline for edge case detection, teams can go beyond standard QA scripts, prevent costly bugs, and deliver a more robust user experience.
Derek Pankaew, CEO & Founder, Listening.com
GPT Automates Code Documentation and Test Cases
I’ll specifically talk about using o1 model along with GPT-4o-Canvas because of their advanced reasoning capabilities and code-specific use cases. Where these models truly shine is in creating the code documentation and test cases. That is why we have internally automated this process. So we can focus on the core development and let the AI handle the documentation and test cases, which can then be reviewed by the testing team. This saved us a lot of hours and saved the developers the trouble of creating the code documentations. A snippet of the code generated by GPT-o1 is as follows:
/**
* Calculates the total tax for an array of items based on category-specific tax rates
* and applicable discounts for bulk purchases.
*
* **Tax Logic:**
* 1. **Tax Rates by Category:**
* – “l1”: 5%
* 2. **Bulk Purchase Discount:**
* – If quantity > 5, reduce tax rate by 2%.
* 3. **Tax Exemptions:**
* – Items marked as tax-exempt incur no tax.
*
* @param {Array<Object>} items – List of items to calculate tax for.
* @param {number} items[].price – Price per unit of the item.
* @param {number} items[].quantity – Quantity of the item.
* @param {string} items[].category – Category of the item.
* @param {boolean} items[].taxExempt – Whether the item is tax-exempt.
*
* @returns {number} Total tax for all items.
*
* @example
*
* const items = [
* { price: 20, quantity: 3, category: ‘food’, taxExempt: false },
* { price: 100, quantity: 6, category: ‘electronics’, taxExempt: false },
* { price: 50, quantity: 2, category: ‘clothing’, taxExempt: true },
*];
*
* const totalTax = calculateTotalTax(items);
*/
This is far more readable and explains everything about how and what the function is supposed to do. And all of this is done in mere seconds.
Gursharan Singh, Co-Founder, WebSpero Solutions
