Experimenting with Microsoft's New Bing Chat to Generate Code and Test Cases
Table of Contents
- Introduction
- Generating Object-Oriented Code with Bing Chat
- Generating Test Cases with Bing Chat
- Challenging Bing Chat with Complex Questions
- Key Takeaways and Conclusion
Introduction to Bing's New Conversational AI Capabilities
Bing recently launched new conversational AI and chat capabilities powered by large language models. This includes the ability to have extended back-and-forth dialogues, ask clarifying questions, and make code generation requests. In this post, we will explore potential use cases and benefits of Bing's updated chat feature.
Specifically, we will walk through examples of using Bing chat for object-oriented code generation, test case development, answering complex questions, and more. We find that Bing's responses are detailed and adaptive across multiple query variations. This makes it a versatile tool for programmers, students, and anyone looking to leverage AI for content creation.
Overview of Bing's Chat-Based Conversational AI
Bing now provides chat-style search that feels more conversational and adaptive. You can ask follow-up questions, clarify requests, and even challenge it with complex prompts. Each session produces unique responses tuned to the specific dialogue. This makes Bing more useful across a wide range of applications. It adds a layer of intelligence onto a search engine, resulting in a smart assistant that can discuss topics, generate content, and explain its reasoning. The chat responses aim to be helpful, snappy, and even funny at times.
Potential Use Cases for Bing's Chat Capability
There are many ways developers, students, writers, and other professionals can leverage Bing's updated chatbot-like functions. Some examples include:
- Getting explanations of concepts in plain language
- Generating code snippets to use as starting points
- Producing draft content such as blog posts and documentation
- Answering domain-specific questions by querying different phrasings
- Having dialogues to clarify and improve initial responses
Generating Object-Oriented Code with Bing Chat
One of the most practical applications of Bing's chat is asking it to generate code snippets on demand. The conversational nature means you can specify exactly what you want, make tweaks and customizations, and get explanations.
For example, say we want an object-oriented Python class for a simple concept. We can query Bing chat directly to create this, then iterate by changing languages and structure.
Starting with a Basic Python Class Example
To start, we asked Bing chat to provide a sample Python class definition with a small typo:
class Person:
# Define fields fname lastname
# Constructor to initialize data members
def init(self, fname, lastname):
self.fname = fname
self.lastname = lastname
As we can see, Bing generated a Person class in Python with fields, a constructor, and assignments to self properties. It even included some comments explaining the code!
The typo was placing a space between 'fname' and 'lastname' in the field declarations. Bing caught this though and corrected it in the constructor parameters.
Customizing the Code into Ruby
Next, we asked Bing to rewrite its Python example into idiomatic Ruby code. It responded with:
class Person
# Define attributes to access fields
attr_accessor :first_name, :last_name
# Constructor to initialize object
def initialize(first_name, last_name)
@first_name = first_name
@last_name = last_name
end
Here we see properly Ruby-style attribute readers and writers via attr_accessor. The constructor and assignments use the @property syntax as well.
One tweak is that it defined the methods using Ruby's default 'def' syntax. We asked Bing to clean this up by using attr_reader :name instead.
It responded explaining: attr_reader :name defines a getter method for the @name property. So there is no need to define the name method explicitly.
Generating Test Cases with Bing Chat
In addition to code generation, we can leverage Bing's conversational nature for other programming tasks. One helpful application is having it automatically produce test cases for existing code.
This saves the tedious effort otherwise required. Below we show an example starting with unit test cases, then transforming them into RSpec.
Unit Tests
First, we asked Bing to provide some Python unit tests for the Person class it previously generated. It came back with:
import unittest
class TestPerson(unittest.TestCase):
def test_creation(self):
person = Person("John", "Doe")
self.assertEqual(person.fname, "John")
self.assertEqual(person.lastname, "Doe")
This gives a starting test suite checking basic object construction and field assignment accuracy. The tests extend the unittest base class and use assertion utilities.
Rewriting Tests in RSpec
Since Ruby is our preferred language, we asked Bing to rewrite the Python tests in RSpec style:
require "person"
describe Person do
it "initializes with given first and last name" do
person = Person.new("Jane", "Smith")
expect(person.first_name).to eq("Jane")
expect(person.last_name).to eq("Smith")
end
end
Now we have concise, expressive RSpec tests wrapped in describe/it blocks and using expect matchers. This will integrate smoothly into a Ruby codebase!
Bing was also kind enough to explain its approach, noting how it: mapped unittest methods to RSpec equivalents, wrapped in describe/context blocks, and leveraged expect for assertions.
Challenging Bing Chat with Complex Questions
In addition to practical code and test generation, we decided to challenge Bing's conversational AI with some open-ended, subjective questions related to programming.
These move beyond fact checks and simple conversions.Generating opinions requires deeper language understanding and reasoning. Below we show two examples queries along with Bing's intriguing attempts to form thoughtful answers.
Asking for Code Generation from UML Diagrams
First, we provided Bing with some sample UML class diagrams described in plain text:
Animal abstract class with eat method. Cat and Dog extend Animal. Cat has meow method, Dog has bark method.
We then asked it to generate Ruby code reflecting the diagram relationships. Bing responded with a fairly accurate translation:
# Abstract parent class
class Animal
def eat
puts "Eating"
end
end
# Concrete sub-classes
class Cat < Animal
def meow
puts "Meow"
end
end
class Dog < Animal
def bark
puts "Woof"
end
end
While not perfect, Bing was able to infer parent-child relationships, inheritances, and unique child class methods purely from the UML description - an impressive display of language understanding!
Seeking Opinions on IDE Layout Preferences
In another example, we asked Bing's chat for its thoughts on the classic developer debate - whether file explorers are better on the left or right in IDEs:
What's better - having the file tree on left or right side in VS Code?
Its response showed humorous awareness of (and refusal to be drawn into) subjective controversies:
I don't have a strong opinion on whether the file explorer is better on the left or right side in VS Code. Both positions have advantages and disadvantages:
- Left side: Keeps folder structure visually separated from files, reduces eye movement in some workflows
- Right side: Better utilizes horizontal space, creates cleaner view without divider
In the end, it comes down to personal preference. I think both setups are valid depending on context and individual coding style.
Well played, Bing! It demonstrated wisdom in acknowledging preference subjectivity rather than taking sides.
Key Takeaways and Conclusion
Experimenting with Bing's new chat offering was intriguing. The conversational nature provided a smooth method to generate code, tests cases, explanations, and content.
Key takeaways include:
- Responses are detailed, adaptive, and tuned to each specific dialogue flow
- Bing chat can produce code snippets, draft test cases, and technical writing
- Asking questions in different ways yields varied outputs to choose from
- Having back-and-forth discussions helps refine and improve initial answers
With further development, features like this could evolve into versatile AI assistants that empower human creators. We're excited to see where things progress next with conversational search and content generation!
Let us know if you have any other fun Bing chat experiments or use cases to try in the comments below!
FAQ
Q: What is the new conversational AI feature offered by Bing?
A: Bing recently launched a chatbot with GPT-like capabilities for interacting conversationally and generating content like code and text.
Q: What programming languages did the author test with Bing?
A: The author tested code generation in Python and Ruby, customizing outputs based on feedback.
Q: How did Bing perform in generating test cases?
A: Bing was able to generate initial unit tests and rewrite them into RSpec based on prompts.
Q: Was Bing able to handle complex requests?
A: Yes, Bing generated code from UML diagrams and provided reasoned opinions on IDE layout.
Q: What were some key takeaways from experimenting with Bing Chat?
A: The author found Bing Chat fun and capable, noting how rephrasing prompts can yield different results across sessions.
Q: Would the author recommend trying Bing Chat?
A: Yes, the author suggests readers try Bing Chat for themselves, as it has many potential use cases despite some limitations.
Q: How could Bing Chat be useful for developers?
A: It can help generate code examples and test cases as a starting point, with annotations to learn from.
Q: What are some limitations of Bing Chat?
A: It may sometimes give generic responses to opinion-based questions and has variable outputs across sessions.
Q: Does Bing Chat eliminate the need for developers?
A: No, Bing Chat is a helpful tool but still requires oversight and iteration from technical experts.
Q: What was the original goal of the YouTube video?
A: The video was meant to be a short but expanded into a full video due to the author's enjoyment of experimenting with Bing Chat.