  Nowadays, development teams often rely on tools such as Jira or Bugzilla to
manage backlogs of issues to be solved to develop or maintain software.
Although they relate to many different concerns (e.g., bug fixing, new feature
development, architecture refactoring), few means are proposed to identify and
classify these different kinds of issues, except for non mandatory labels that
can be manually associated to them. This may lead to a lack of issue
classification or to issue misclassification that may impact automatic issue
management (planning, assignment) or issue-derived metrics. Automatic issue
classification thus is a relevant topic for assisting backlog management. This
paper proposes a binary classification solution for discriminating bug from non
bug issues. This solution combines natural language processing (TF-IDF) and
classification (multi-layer perceptron) techniques, selected after comparing
commonly used solutions to classify issues. Moreover, hyper-parameters of the
neural network are optimized using a genetic algorithm. The obtained results,
as compared to existing works on a commonly used benchmark, show significant
improvements on the F1 measure for all datasets.
