Geração automática de relatórios de confiabilidade: projeto e implementação da camada front-end para identificação de causas de falhas de software na plataforma X-RAT

Abstract

Operating systems such as Windows 10 and 11 support critical activities in both personal and corporate environments, so failures in this software can compromise service availability and user experience. In this context, it becomes relevant to systematically analyze failure events and structure information that supports the diagnosis of root causes and the improvement of the reliability of the evaluated computers.

This work presents the development and enhancement of the X-RAT platform (X-Reliability Analysis Tool), a tool designed for the analysis and automatic generation of software reliability reports in operating systems (OSs), focusing on Windows 10 and 11. The platform collects logs, extracts failure events, stores the data in a database, and processes them through failure categorization algorithms based on criteria such as kernel failures (OSₖₙₗ), service failures (OSₛᵥ𝒸), system application failures (OSₐₚₚ), and user application failures (USRₐₚₚ). In addition, several statistical metrics are computed, including the frequency of failure causes, the distribution of failures by category, and the occurrence of these categories throughout the day (Early Morning, Morning, Afternoon, and Evening) and the days of the week.

Finally, based on the times between failures, reliability metrics such as MTBF (Mean Time Between Failures) and MTTF (Mean Time To Failure) are applied, as well as analyses based on probability distributions. Each analysis is formalized in a report automatically generated by the platform.

The goal of this work is to fully refactor the previously developed platform, which is currently outdated and incomplete. The new version will feature additional functionalities, including automatic failure collection and more accurate diagnostics of failure causes, enabling deeper analysis and contributing to more informed decision-making in improving the reliability of the analyzed computers.