Classifying Fault Category and Severity of UAV Flight Controllers’ Reported Issues

Unmanned Ariel Vehicles (UAVs) have gained significant importance in diverse sectors. Thus, a profound safety risk analysis/assessment to prevent any possible damage to themselves, the environment, and humans is fundamental for building and utilizing UAVs. To achieve that, two fundamental challenges should be addressed: i) identification of types and frequency of the issues and ii) assessment of their impact. In this paper, we aim to address the first challenge by automatizing the process of data field analysis. To do so, we first performed some statistical analysis of the reported issues of UAV systems (in Github) and manually extracted detailed data from the reports to better understand the type and nature of the issues. Then, to automatize the analysis, we used natural language processing algorithm to extract the keywords from the reports, and then applied four machine learning algorithms to build classifier models to classify the reports according to the fault category and severity level. The good performance results obtained suggest that these analyzes can be performed to further understand the UAV system issues, and help in the risk assessment procedure to identify the hazard and define the frequency and severity of the risk. Moreover, the results of this work can help a big community of developers and researchers in the precise and fast analysis of bug reports and safety risk assessment of any software system.

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