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Automated unit test creation based on Artificial Intelligence

Marco Sorbi

Automated unit test creation based on Artificial Intelligence.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024

Abstract:

This thesis explores the application of Generative Artificial Intelligence, specifically Large Language Models (LLMs), for the automated generation of C++ unit tests, addressing the common issue of projects being developed without sufficient testing. The study focuses on overcoming LLM challenges, such as context limitation and low reliability, by developing an Adaptive Focal Context procedure for C++, to select information to use in LLM queries for generating unit tests, and implementing a check-repair mechanism to fix incorrect unit tests. The created system is validated using various LLMs, obtaining results comparable to previous studies for other languages. Despite overall positive results, challenges related to handling certain C++ functionalities and packages emerged, suggesting areas for future research. The thesis concludes with suggestions for system improvements and strategies to address identified issues.

Relators: Paolo Garza
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 61
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Ente in cotutela: INSTITUT EURECOM (FRANCIA)
Aziende collaboratrici: AMADEUS SAS
URI: http://webthesis.biblio.polito.it/id/eprint/31118
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