High Level Description

Software testing is a critical component of the development process, ensuring the quality and reliability of applications. However, creating comprehensive test suites is often time-consuming, labor-intensive, and prone to human error. As software systems grow in complexity, there is an increasing need for more efficient and effective testing methods. AI-powered test suite generation presents a promising solution to streamline the testing process, potentially improving both efficiency and coverage while reducing the burden on human testers.

Project Description

The primary objective of this project is to develop an AI model capable of automatically generating effective test suites for software applications. The research will explore various AI approaches and evaluate their performance in creating high-quality test cases. The project will involve the following key components:

Compile a diverse dataset of software specifications, requirements, and corresponding test suites.

Investigate and implement different AI techniques for test suite generation, including but not limited to:

• Large Language Models
• Genetic Algorithms
• Reinforcement Learning approaches
• Custom neural network architectures

Train the selected AI models on the curated dataset, experimenting with various hyperparameters and training strategies to optimize performance.

Develop a system that takes software specifications as input and generates comprehensive test suites using the trained AI model.
Assess the quality of the AI-generated test suites.

Who are we looking for?

Bachelor/Master of Science in Computer Science/Engineering

Purpose

The purpose of this research is to advance the field of automated software testing by exploring the potential of AI in generating high-quality test suites. This could potentially contribute to the broader discussion on the role of AI in software engineering and quality assurance.