Abstract
While constructing supervised learning models, we require labeled examples to build a corpus and train a machine learning model. However, most studies have built the labeled dataset manually, which, on 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 labeling 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 labeling 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 annotating the corpus for supervised classifiers. Apart from assisting researchers in crowdsourcing a labeled dataset, the tool has practical applicability in software engineering education and assists in building expert ratings for software artefacts.
Published Version
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