  Software development tasks must be performed successfully to achieve software
quality and customer satisfaction. Knowing whether software tasks are likely to
fail is essential to ensure the success of software projects. Issue Tracking
Systems store information of software tasks (issues) and comments, which can be
useful to predict issue success; however; almost no research on this topic
exists. This work studies the usefulness of textual descriptions of issues and
comments for predicting whether issues will be resolved successfully or not.
Issues and comments of 588 software projects were extracted from four popular
Issue Tracking Systems. Seven machine learning classifiers were trained on 30k
issues and more than 120k comments, and more than 6000 experiments were
performed to predict the success of three types of issues: bugs, improvements
and new features. The results provided evidence that descriptions of issues and
comments are useful for predicting issue success with more than 85% of accuracy
and precision, and that the predictions of issue success vary over time. Words
related to software development were particularly relevant for predicting issue
success. Other communication aspects and their relationship to the success of
software projects must be researched in detail using data from software tools.
