Rationale in Development Chat Messages: An Exploratory Study

Chat messages of development teams play an increasingly significant role in software development, having replaced emails in some cases. Chat messages contain information about discussed issues, considered alternatives and argumentation leading to the decisions made during software development. These elements, defined as rationale, are invaluable during software evolution for documenting and reusing development knowledge. Rationale is also essential for coping with changes and for effective maintenance of the software system. However, exploiting the rationale hidden in the chat messages is challenging due to the high volume of unstructured messages covering a wide range of topics. This work presents the results of an exploratory study examining the frequency of rationale in chat messages, the completeness of the available rationale and the potential of automatic techniques for rationale extraction. For this purpose, we apply content analysis and machine learning techniques on more than 8,700 chat messages from three software development projects. Our results show that chat messages are a rich source of rationale and that machine learning is a promising technique for detecting rationale and identifying different rationale elements.Comment: 11 pages, 6 figures. The 14th International Conference on Mining Software Repositories (MSR'17

Along the Margins: Marginalised Communities' Ethical Concerns about Social Platforms

In this paper, we identified marginalized communities' ethical concerns about social platforms. We performed this identification because recent platform malfeasance indicates that software teams prioritize shareholder concerns over user concerns. Additionally, these platform shortcomings often have devastating effects on marginalized populations. We first scraped 586 marginalized communities' subreddits, aggregated a dataset of their social platform mentions and manually annotated mentions of ethical concerns in these data. We subsequently analyzed trends in the manually annotated data and tested the extent to which ethical concerns can be automatically classified by means of natural language processing (NLP). We found that marginalized communities' ethical concerns predominantly revolve around discrimination and misrepresentation, and reveal deficiencies in current software development practices. As such, researchers and developers could use our work to further investigate these concerns and rectify current software flaws

An Exploratory Study of Twitter Messages about Software Applications

Users of the Twitter microblogging platform share a considerable amount of information through short messages on a daily basis. Some of these so-called tweets discuss issues related to software and could include information that is relevant to the companies developing these applications. Such tweets have the potential to help requirements engineers better understand user needs and therefore provide important information for software evolution. However, little is known about the nature of tweets discussing software-related issues. In this paper, we report on the usage characteristics, content and automatic classification potential of tweets about software applications. Our results are based on an exploratory study in which we used descriptive statistics, content analysis, machine learning and lexical sentiment analysis to explore a dataset of 10,986,495 tweets about 30 different software applications. Our results show that searching for relevant information on software applications within the vast stream of tweets can be compared to looking for a needle in a haystack. However, this relevant information can provide valuable input for software companies and support the continuous evolution of the applications discussed in these tweets. Furthermore, our results show that it is possible to use machine learning and lexical sentiment analysis techniques to automatically extract information about the tweets regarding their relevance, authors and sentiment polarity

A Needle in a Haystack: What Do Twitter Users Say about Software?

Users of the Twitter microblogging platform share a vast amount of information about various topics through short messages on a daily basis. Some of these so called tweets include information that is relevant for software companies and could, for example, help requirements engineers to identify user needs. Therefore, tweets have the potential to aid in the continuous evolution of software applications. Despite the existence of such relevant tweets, little is known about their number and content. In this paper we report on the results of an exploratory study in which we analyzed the usage characteristics, content and automatic classification potential of tweets about software applications by using descriptive statistics, content analysis and machine learning techniques. Although the manual search of relevant information within the vast stream of tweets can be compared to looking for a needle in a haystack, our analysis shows that tweets provide a valuable input for software companies. Furthermore, our results demonstrate that machine learning techniques have the capacity to identify and harvest relevant information automatically