Description:

Speaker: Syed Asad

Abstract: In this talk, I will present CodiumAI Cover Agent, an innovative tool that leverages advanced Generative AI to automate the generation of unit tests, significantly enhancing code coverage and quality assurance. This talk will delve into the features, installation, usage, and development roadmap of Cover Agent, highlighting how it integrates with various CI platforms and supports multiple programming languages. By sharing practical insights and live demonstrations, attendees will learn how to streamline their development workflows and achieve comprehensive test coverage effortlessly.

Proposal Details: 1. Introduction and Motivation:

Overview of the challenges in achieving high code coverage and maintaining comprehensive test suites. Introduction to CodiumAI Cover Agent and its goal of automating and enhancing unit test generation using Generative AI. 2. Key Features of CodiumAI Cover Agent:

Test Runner: Executes the command or scripts to run the test suite and generate code coverage reports. Coverage Parser: Validates code coverage increases as tests are added. Prompt Builder: Gathers necessary data from the codebase and constructs prompts for the LLM. AI Caller: Interacts with the LLM to generate tests based on the provided prompts. 3. Installation and Usage:

Requirements: Necessary environment setup, including setting API keys and installing coverage tools. Installation Options: Installing as a Python Pip package or standalone executable. Running the Code: Detailed instructions on executing Cover Agent with various programming language examples (Python, Go, Java). 4. Integration with CI Platforms:

Plans for integrating Cover Agent with popular CI platforms (GitHub Actions, Jenkins, CircleCI, Travis CI). Benefits of seamless CI integration for automated test generation and validation. 5. Development and Contribution:

Encouragement for community collaboration and extending Cover Agent's capabilities. Overview of the current development status and future roadmap, including support for more programming languages, test scenarios, and usability improvements. 6. Real-World Applications and Benefits:

Case studies and examples demonstrating the effectiveness of Cover Agent in real-world projects. Discussion on the impact of automated test generation on development efficiency and software quality. 7. Live Demonstration:

Step-by-step live demo showcasing the installation, configuration, and execution of Cover Agent. Generating and validating unit tests for a sample project, highlighting the ease of use and effectiveness of the tool. 8. Q&A Session:

Open floor for questions and discussions, addressing any queries from the audience about Cover Agent and its applications.

Prerequisites:

Pre-session Requirements for Participants: 1. Basic Understanding of Python:

Familiarity with Python programming is essential as the session will include examples and demonstrations using Python code. 2. Knowledge of Unit Testing:

Participants should have a basic understanding of unit testing principles and practices. Prior experience with unit testing frameworks like pytest will be beneficial. 3. Experience with Command Line Interface (CLI):

The session will involve running commands via CLI, so participants should be comfortable with using the command line. 4. Awareness of Code Coverage Tools:

Familiarity with code coverage tools and reports, such as Cobertura or pytest-cov, will help participants understand the coverage validation process discussed in the session. 5. Interest in AI and Automation:

An interest in AI, particularly in how it can be leveraged to automate software development tasks, will enhance the learning experience. 6. Pre-installed Tools (Optional but Recommended):

Python: Ensure Python is installed on your system. Poetry: For managing Python package dependencies. Installation instructions can be found here. Cover Agent Package: Optional, but installing the Cover Agent package beforehand can help follow along with the demonstrations. Instructions are provided in the session. 7. Questions and Curiosity:

Come prepared with questions or specific scenarios where you believe automated unit test generation could be beneficial. This will help make the Q&A session more interactive and tailored to participants' needs. By ensuring familiarity with these areas, participants will be better equipped to fully engage with the session and apply the concepts and tools discussed to their own projects.

Video URL:

https://www.loom.com/share/bd13165b45ac44899d8feeae3378c79d?sid=cff546f1-0682-427e-bc44-8278652e47c2

Content URLs:

NA

Speaker Info:

Syed Asad, is the Lead Artificial Intelligence Engineer, working at KiwiTech LLC. He is a seasoned AI & Machine Learning Researcher, with experience more than a decade in Amazon and other Companies. Focus Areas: Large Language Models, Generative AI, LLM Apps for Production, Cost Optimisation Architecture etc.

Speaker Links:

GitHub: https://github.com/syedzaidi-kiwi LinkedIN: https://www.linkedin.com/in/syed-asad-76815246/