{'fraction_non_alphanumeric': 0.02573266619013581, 'fraction_numerical': 0.0, 'mean_word_length': 5.542056074766355, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
EmoD: An End-to-End Approach for Investigating Emotion Dynamics in Software Development

Emotions are an integral part of human nature. Emotion awareness is critical to any form of interpersonal communication and collaboration, including these in the software development process. Recently, the SE community starts having growing interests in emotion awareness in software development. While researchers have accomplished many valuable results, most extant research ignores the dynamic nature of emotion. To investigate the emotion dynamics, SE community needs an effective approach to capture and model emotion dynamics rather than focuses on extracting isolated emotion states. In this paper, we proposed such an approach–EmoD. EmoD is able to automatically collect project teams' communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We developed a prototype tool that instantiates the EmoD approach by assembling state-of-the-art NLP, SE, and time series techniques. We demonstrate the utility of the tool using the IPython's project data on GitHub and a visualization solution built on EmoD. Thus, we demonstrate that EmoD can provide end-to-end support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation.