Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

This paper reports data from three sources showing young people’s positive responses to the population-wide Act-Belong-Commit mental health promotion campaign: (i) impact on 14-25 year old participants in a peer-educator workshop program; (ii) impact on 11-14 year old student participants in an adaptation of the campaign in secondary schools (the ‘Mentally Healthy Schools Framework’); and (iii) impact of the population wide media-based campaign on 18-24 and 25-34 year olds in the general population (versus those 35+ years). Overall, these findings support the conclusion that mental health promotion interventions can be based on underlying constructs relevant across the lifespan and then tailored for specific ages and settings rather than requiring the development of separate, distinct programs based on different constructs for younger age groups.