  Effective issue triaging is crucial for software development teams to improve
software quality, and thus customer satisfaction. Validating issue reports
manually can be time-consuming, hindering the overall efficiency of the
triaging process. This paper presents an approach on automating the validation
of issue reports to accelerate the issue triaging process in an industrial
set-up. We work on 1,200 randomly selected issue reports in banking domain,
written in Turkish, an agglutinative language, meaning that new words can be
formed with linear concatenation of suffixes to express entire sentences. We
manually label these reports for validity, and extract the relevant patterns
indicating that they are invalid. Since the issue reports we work on are
written in an agglutinative language, we use morphological analysis to extract
the features. Using the proposed feature extractors, we utilize a machine
learning based approach to predict the issue reports' validity, performing a
0.77 F1-score.
