| KEYWORD |
Area Engineering
Enhancing Software Testing Through Generative AI
keywords AI, AUTOMOTIVE, TESTING
Reference persons RICCARDO COPPOLA
Research Groups DAUIN - GR-16 - SOFTWARE ENGINEERING GROUP - SOFTENG
Thesis type INDUSTRY
Description Thesis work at Concept Quality Reply
Context:
In the specific domain of vehicle diagnostics and IoT, achieving thorough End-to-End (E2E) validation is critical for ensuring the reliability and
performance of integrated systems. This necessitates innovative approaches to software testing, and Generative Artificial Int elligence (Generative AI)
emerges as a promising tool to streamline and augment testing processes.
Description:
This thesis delves into the integration and application of Generative AI in the field of software testing. y harnessing the p ower of Generative AI, this
research aims to enhance the efficiency, accuracy, and comprehensiveness of testing activities, especially within the context of IoT and connected
vehicles.
Objectives:
• Explore the potential of Generative AI in generating comprehensive and diverse test cases for intricate software systems.
• Investigate how Generative AI can expedite the creation of test scenarios and data, mimicking real-world usage and improving test coverage.
• Assess the impact of Generative AI on the overall efficiency and effectiveness of End-to-End (E2E) validation processes, particularly in IoT and
connected vehicle systems.
• Develop methodologies to integrate Generative AI seamlessly into the software testing life cycle, with a focus on optimizing resource utilization and
reducing testing time.
• Pre-requisites:
• Proficiency in software development and testing methodologies.
• Familiarity with Generative AI concepts and principles.
• Understanding of IoT and connected vehicle technologies.
• Skills acquired:
• Java, Postman, Selenium, CANalyzer, CAPL, Generative AI frameworks (e.g., GPT-3, GPT-4), Python, Machine Learning libraries (e.g.,
TensorFlow, PyTorch), IoT frameworks and protocols, Connected vehicle technologies, Automated testing tools.
Deadline 03/09/2025
PROPONI LA TUA CANDIDATURA