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.

During software maintenance, bug reports are widely employed to improve the software project’s quality. A developer often refers to stowed bug reports in a repository for bug resolution. However, this reference process often requires a developer to pursue a substantial amount of textual information in bug reports which is lengthy and tedious. Automatic summarization of bug reports is one way to overcome this problem. Both supervised and unsupervised methods are effectively proposed for the automatic summary generation of bug reports. However, existing methods disregard the significance of duplicate bug reports in summarizing bug reports. In this study, we propose a PageRank-based Summarization Technique (PRST), which utilizes the textual information contained in bug reports and additional information in associated duplicate bug reports. PRST uses three variants of PageRank-based on Vector Space Model (VSM), Jaccard, and WordNet similarity metrics. These variants are utilized to calculate the textual similarity of the sentences between the master bug reports and their duplicates. PRST further trains a regression model and predicts the probability of sentences belonging to the summary. Finally, we combine the values of PageRank and regression model scores to rank the sentences and produce the summary for the master bug reports. In addition, we construct two corpora of bug reports and duplicates, i.e. MBRC and OSCAR. Empirical results suggest that PRST outperforms the state-of-the-art method BRC in terms of Precision, Recall, F-score, and Pyramid Precision. Meanwhile, PRST with WordNet achieves the best results against PRST with VSM and Jaccard.