Year: 2026
Type: dissertation
Abstract: The success of a software project depends not only on completing the implementation, but also on the system operating correctly and reliably under real-world conditions.Debugging is a critical part of this process, but it is often time-consuming and requires significant effort from developers, especially in complex software systems.When an automated test fails, the test runner reports the failure but does not explain its cause, which must then be identified manually in the application logs.This thesis focuses on the interpretation of such failures.A pipeline is developed that correlates user interface (UI) test results with the application's unstructured event logs over the same time window, without modifying the system under test.At design level, the same approach can also be extended to logs collected during production use.An Extract, Transform, Load (ETL) mechanism implemented in Python runs after the tests complete, collects the produced records, and applies cleaning, formatting, and personal-data protection.If at least one test has failed, the processed records are provided as input to a general-purpose large language model (LLM), which generates a structured diagnosis containing a likely cause of the failure, supporting evidence from the logs, and suggested steps for further investigation.The diagnosis is presented as a report available to stakeholders, so that they can take necessary actions.The evaluation was carried out qualitatively on a limited set of controlled scenarios, which include both a successful run and different types of failure.The proposed system does not detect the failure, since the test tool already reports it.The benefit is that the developer receives a structured diagnosis with a likely cause, supporting evidence, and suggested next steps, instead of starting a manual investigation from raw logs.
Author: Γιαννακοσιάν, Παναγιώτης Ι.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223534937