PartyRock: AI Python Code Checker for Tech Interview Simulation

This article was written by Kayne Rodrigo. Kayne is a 4th-year BS Computer Science student at Pamantasan ng Lungsod ng Maynila (PLM). He serves as an IT Intern at Tutorials Dojo and a Data & Impact Junior Mission Specialist at KadaKareer. He actively contributes to the student tech community by being a student tech lead at AWS Cloud Club Haribon.

Are you a recent college graduate or an individual making a career shift into technology, aiming to excel in coding interviews that emphasize Python? Python is undeniably one of the most popular and versatile programming languages in the tech industry.

You may have tried different approaches to learning Python, but feel overwhelmed by the numerous study methods and coding bootcamps out there.

Additionally, many of the best resources available often come with a subscription fee. But what if you could use Generative AI to review your Python code according to industry standards? The great news is that, as of 2024, it's completely FREE!

Before we start developing our Python Code Checker software, let’s first explore the foundational technology behind it.

What is PartyRock?

PartyRock, developed by Amazon Web Services (AWS), allows users to create AI-powered solutions using Amazon Bedrock, providing access to Natural Language Processing (NLP) models from companies such as Amazon, Anthropic, AI21 Labs, Cohere, Meta, Mistral AI, and Stability AI.

In this case, coding skills are not required – only strong prompt engineering skills, which will be explained later in this blog.

In addition, PartyRock provides a platform where users can test and develop AI applications using the robust tools of Amazon Bedrock, making it easier for anyone to build AI solutions.

At this point, it’s clear that PartyRock can generate AI-driven solutions through prompt engineering skills alone. However, the accuracy of Python code analysis depends on the resources selected for reference during the PartyRock prompting process.

AI Code Checker Tools Comparison

Why does PartyRock stand out? To gain a better understanding of the benefits of PartyRock as a Code Checker, let’s compare it with other AI-powered tools that evaluate Python code quality. For this article, a brief study was conducted on the most popular AI Code Checker tools available. Below are the most noteworthy ones:

1. Amazon CloudGuru

Amazon CloudGuru is another coding assistant service from Amazon Web Services (AWS) that identifies inefficient lines of code in terms of memory usage and time complexity. It assists developers in analyzing the runtime performance of their code, particularly when deployed through the AWS Console.

• Pros: It integrates seamlessly with code deployed on AWS services, scales easily, and offers valuable insights into both code quality and performance.

• Cons: Primarily designed for AWS systems, limiting its use for tasks outside of AWS.

2. DeepSource

DeepSource is a contemporary static analysis platform that uses AI to detect errors and recommend improvements for code quality. It helps development teams consistently follow coding best practices.

• Pros: It supports multiple programming languages, offers easy integration with CI/CD pipelines, and provides valuable recommendations.

• Cons: Certain code assistance features may require a paid account.

3. Codacy

Codacy is an automated source code analyzer that detects potential issues, particularly during production deployment. It provides automatic code checks, style assessments, and security analysis using AI.

• Pros: It can assess code quality and security across multiple languages, with customizable rules and detailed reports.

• Cons: The free version offers limited features, and the interface may be confusing for new users.

4. Snyk

Snyk is a security-focused code quality checker designed to evaluate code security before the next pull request commit. It is known for its speed and accuracy, generating fewer false positives, which helps developers quickly address issues and create more secure software.

• Pros: It emphasizes security, integrates seamlessly with development workflows, and offers actionable solutions.

• Cons: It primarily focuses on security, potentially overlooking some aspects of code quality.

5. TabNine

TabNine is an AI-powered coding assistant that helps produce higher quality software through features like code generation, testing, and automated code reviews tailored to the engineering team. It serves as an AI code completion tool, offering suggestions to enhance code quality.

• Pros: Boosts productivity with intelligent code completions and is compatible with a wide range of IDEs.

• Cons: Primarily focuses on code completion rather than a comprehensive code quality review.

6. Python Code Checker with PartyRock by AWS

PartyRock by AWS, in terms of Python code analysis, can evaluate how effectively a Python code solution addresses the given problem, enabling better analysis.

It was created using the Role, Instructions, Context, Constraints, and Examples (RICCE) Prompt Engineering Framework to define the requirements for technical interviews.

Additionally, leveraging the available Amazon Bedrock Foundational Models, it can effectively assess code quality and provide valuable suggestions for improvement.

• Pros: It is free to use, offers a highly customizable user experience based on the use case, and provides intelligent insights through its integrated Amazon Bedrock Foundational Models.

• Cons: Although free of charge, the trial has limited usage and is available only for a limited time, with no specified end date.

Comparative Analysis of AI Checker Software

In summary, I have gathered all the observations into the comparative analysis matrix below, focusing on the common features shared by all AI checker software.

As shown, although PartyRock is available for a limited time, its benefits are evident. It serves as a fully customizable generative AI web app that can be tailored to your specific use case. Not only can it function as a Python code analyzer, but additional features can be added as long as they meet your precise requirements.

To keep things simple and user-friendly, the focus was solely on Python for the code analyzer. However, it can also be customized to support other programming languages, including Java, JavaScript, SQL, Docker syntax, and more!

Once again, it all depends on how the prompts are generated, as PartyRock by AWS relies heavily on the application's description to be created.

Next, let’s explore the features and topics you can incorporate into your prompt before we dive into the hands-on exercise.

This web app is an example based on the prompt created, which will be provided during the hands-on exercise.

UI Components of our PartyRock-based Python Code Checker

The app is called “PythonCodingAI: Your Path to Polished Python Proficiency”

• This application features a single-page, minimalist UI designed to be highly intuitive.

• The process will only include:

1. Pasting the problem for context on the left widget.

2. Pasting the source code on the right widget.

3. Click the succeeding widgets to operate.

• Once the web app is opened, it can be used immediately on your end.

• The PartyRock-based Python Code Checker app can be accessed here.

The list below provides a description of the UI components of this web app.

1. AI Code Reviewer Introduction

• It simply describes what the web app is all about.

2. Code Problem Description

• This is where the user provides the problem description to give better context, allowing the AI to analyze the most effective approach based on the problem and its mechanics.

3. Python Code Input

• This is where the user enters the Python source code. While it doesn't yet support IDE-based code input with line indexing, the code can simply be pasted, and the AI will be able to analyze it.

4. Code Quality Analysis

• This section contains feedback based on the code problem and input provided earlier. The criteria for analyzing code quality will be discussed later in this article.

5. Big O Notation and Score

• This section provides overall feedback based on the guidelines set in the prompt. It includes an estimated complexity notation along with a code quality score.

• After reviewing this, you can make note of it and adjust your coding structure accordingly.

6. Reveal Cleaned Code

• This section includes the revised code that adheres to the best practices outlined in the prompt. Use it only to cross-check your code afterward.

Having covered the key UI/UX components of our Generative AI-based Python Code Checker solution, let's now discuss the mechanics used in creating the AI prompt within PartyRock on AWS.

Python Interview Topics Covered by the PartyRock App

When preparing for a Python-based technical interview, the first step is to understand the criteria the hiring manager will use to assess the structure of your Python code.

Adhering to good coding practices is crucial in all key areas of Python, such as basic syntax, object-oriented programming, and data structures and algorithms.

To guide preparation, research was conducted on these aspects, and five key areas were identified for focus:

1. Code Readability

• Treating coding as an art involves making it readable. The code should adhere to a widely recognized coding style known as PEP-8.

> It uses an indentation of 4 spaces, restricts line length to 79 characters, and follows naming conventions for each Python component.

• Employing a meaningful and clear naming convention improves readability and makes it easier for others, like colleagues, to comprehend.

• Consistent naming conventions are crucial during code interviews and in production environments, ensuring that the code remains clear and maintainable.

2. Modularity and Maintainable Code Structure

• Writing efficient modular code involves breaking down programs into reusable functions and classes.

• Avoid the practice of writing duplicate code by following the Don’t Repeat Yourself (DRY) principle.

• Keep variable scopes manageable and minimize the use of global variables to prevent potential misuse or runtime issues.

• In general, a modular code structure improves maintainability, simplifies testing, and makes debugging more effective.

3. Effective Documentation and Comments

• This involves using docstrings effectively for modules, classes, and functions to describe their purpose, parameters, expected return values, and any exceptions.

• Use comments judiciously to explain unclear functionalities in your code.

• Developing a habit of thorough documentation helps in understanding the code's functionality and promotes effective communication within the development team.

4. Robust Error Handling and Testing

• Implement error handling properly by strategically using try-except blocks, catching specific exceptions, and managing errors in a graceful manner.

• Learn unit testing techniques, such as using a Python framework like Pytest, to verify that each component of your code functions as expected.

• All the non-functional requirements for a specific module are operational.

5. Optimization and Best Practices

• Choosing the appropriate data structures for a given problem.

• Evaluating the code's complexity with Big O notation.

• Leveraging Python libraries rather than building everything from the ground up.

• Carefully restricting mutable arguments when defining functions.

• Utilizing Python virtual environments to prevent dependency conflicts between projects.

• Employing version control tools such as Git and GitHub to safeguard your code.

Although these guidelines serve as the foundation for creating projects suitable for production deployment, many developers still struggle to follow these practices.

Therefore, utilizing AI to address these gaps will undoubtedly assist applicants in successfully passing their tech interviews.

Now, let's move on to the potential use cases of the PartyRock-based Python Code Checker.

Use-cases and Applications of PartyRock-based Python Code Checker

1. Interview simulation practice

• The application can be used to simulate a tech interview, allowing you to practice solving a challenging Python programming problem.

• It’s important to approach the PartyRock app’s explanation of its code analysis with a healthy dose of skepticism.

2. Real-time code review

• Using the PartyRock app, you can conduct a real-time code review of a Python problem, analyze subtle changes in the code, and identify the correct solution.

3. Algorithm Optimization

• You can assess your coding shortcomings in the next step and work on improving your code.

• Since the app runs solely with Generative AI, it provides a low-risk environment for errors, helping you minimize mistakes during the tech interview.

If you'd like to learn how to create your own Generative AI web app using PartyRock, you can try the 'Hands-on Exercise: Getting Started with PartyRock' here. This doesn’t require coding, but it does involve some knowledge of prompt engineering.

Final Remarks

It’s fairly straightforward, isn’t it? By now, you’ve already built an AWS Generative AI project that you can add to your portfolio as part of your preparation for tech interviews.

As you can see, prompt engineering and PartyRock were used to develop a straightforward yet effective solution. This approach can also be applied in Generative AI-based hackathons, should you choose to participate.

To take your skills to the next level, consider applying your Generative AI knowledge by taking the newly launched AWS AI Practitioner exam. Don’t worry about the exam's challenges, as Tutorials Dojo has you covered!

• Tutorials Dojo provides a detailed overview blog for the AIF-C01 exam, which can be read here.

• You can also purchase their newly released AIF-C01 practice exam, available here.

* This newsletter was sourced from this Tutorials Dojo article.

* For more learning resources, you may visit:

• Free PlayCloud Guided Labs

• Free AWS Digital Courses

• Free Practice Exams