  While constructing supervised learning models, we require labelled examples
to build a corpus and train a machine learning model. However, most studies
have built the labelled dataset manually, which in many occasions is a daunting
task. To mitigate this problem, we have built an online tool called
CodeLabeller. CodeLabeller is a web-based tool that aims to provide an
efficient approach to handling the process of labelling source code files for
supervised learning methods at scale by improving the data collection process
throughout. CodeLabeller is tested by constructing a corpus of over a thousand
source files obtained from a large collection of open source Java projects and
labelling each Java source file with their respective design patterns and
summaries. Twenty five experts in the field of software engineering
participated in a usability evaluation of the tool using the standard User
Experience Questionnaire online survey. The survey results demonstrate that the
tool achieves the Good standard on hedonic and pragmatic quality standards, is
easy to use and meets the needs of the annotating the corpus for supervised
classifiers. Apart from assisting researchers in crowdsourcing a labelled
dataset, the tool has practical applicability in software engineering education
and assists in building expert ratings for software artefacts.
