A key issue in collaborative software development is communication among developers. One modality is commit messages, which allow developers to describe the changes they make. As such, commit messages serve as an ``audit trail'' by which developers can understand how the source code of a project has changed - and why. Hence, the quality of commit messages affects the communication among developers. Developers often write poor-quality commit messages due to a lack of time and motivation. Several automatic approaches have been proposed to generate commit messages. However, these are based on uncurated datasets including considerable proportions of poorly phrased commit messages. In this multi-method study, we first define what constitutes a ‘‘good’’ commit message, and then establish what proportion of commit messages lack information using a sample of almost 1,600 messages from five highly active open source projects. We find that an average of circa 40% of messages could be improved, suggesting the use of uncurated datasets may be a major threat when commit message generators are trained with such data. We also observe that prior work has not considered the semantics of commit messages, and there is little guidance available for writing good commit messages. To that end, we develop a taxonomy based on recurring patterns in commit messages’ expressions. Finally, we investigate whether ‘‘good’’ commit messages can be automatically identified, which can be used to prompt developers to write better commit messages.