In this paper, we present an analysis of more than 500K comments from open-source repositories of software systems. Our aim is to empirically determine how developers interact with each other under certain psychological conditions generated by politeness, sentiment and emotion expressed in developers' comments. Developers involved in open-source projects do not usually know each other; they mainly communicate through mailing lists, chat rooms, and tools such as issue tracking systems. The way in which they communicate affects the development process and the productivity of the people involved in the project. We evaluated politeness, sentiment, and emotions of comments posted by developers and studied the communication flow to understand how they interacted in the presence of impolite and negative comments (and vice versa). Our analysis shows that when in presence of impolite or negative comments, the probability of the next comment being impolite or negative is 14 and 25 , respectively; anger, however, has a probability of 40 of being followed by a further anger comment. The result could help managers take control the development phases of a system since social aspects can seriously affect a developer's productivity. In a distributed environment this may have a particular resonance.
Several recent studies have demonstrated the importance and relationship of productivity and quality to human aspects associated with the software development process. studied the effect of politeness @cite_0 @cite_42 and emotions @cite_43 on the time required to fix any given issue. The authors demonstrated that emotions did have an effect on the issue fixing time. Namely, results showed that issue fixing time for polite issues was shorter than issue fixing time for impolite and mixed-issues (polite and impolite issues). If someone is asked to accomplish a task in a polite way, there is higher possibility for a relaxed collaboration and faster results. On the other hand, impolite requests can easily generate discomfort, stress and burnout, often negatively impacting the actual time taken to complete a given task. The study also showed that a positive correlation existed between the percentage of polite comments and Magnetism and Stickiness of a project (higher attractiveness) and that the percentage of polite comments over time was (for the majority of the projects in our corpus) seasonal and not random.
@cite_0 @cite_43 @cite_42
2617883065 2623177746 2779931473 2157163421
The role of emotions and human aspects of software development is gaining increasing attention from both academics and developers. Software development is a complex activity, but since software is everywhere and it is necessary to achieve the highest quality possible. High technical skills, knowledge and competence of a software developer are not the only factors which influence the final product. Good attitudes, communication skills and good manners are equally as important. In this paper we focused our attention on understanding how developer sentiment and emotions evolved over time during the development process of 10 systems, considering affectiveness as time series. We studied seasonality, randomness and correlations. Results showed that there was not a significant correlation among joy and love, sadness and anger, sentiment and joy. Regarding seasonality and randomness, results showed that in the majority of the cases, the analysed time series are seasonal and not random. Software development—just like any other human collaboration—inevitably evokes emotions like joy or sadness, which are known to affect the group dynamics within a team. Today, little is known about those individual emotions and whether they can be discerned at all in the development artifacts produced during a project. This paper analyzes (a) whether issue reports—a common development artifact, rich in content—convey emotional information and (b) whether humans agree on the presence of these emotions. From the analysis of the issue comments of 117 projects of the Apache Software Foundation, we find that developers express emotions (in particular gratitude, joy and sadness). However, the more context is provided about an issue report, the more human raters start to doubt and nuance their interpretation. Based on these results, we demonstrate the feasibility of a machine learning classifier for identifying issue comments containing gratitude, joy and sadness. Such a classifier, using emotion-driving words and technical terms, obtains a good precision and recall for identifying the emotion love, while for joy and sadness a lower recall is obtained. Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a “dream” team involves many variables, including consideration of human factors and it is not a dilemma solvable in a mathematical way. Empirical studies might provide interesting insights to explain which factors need to be taken into account in building a team of developers and which levers act to optimise productivity among developers. In this paper, we present the results of an empirical study aimed at investigating the link between team diversity (i.e., gender, nationality) and productivity (issue fixing time). We consider issues solved from the GHTorrent dataset inferring gender and nationality of each team’s members. We also evaluate the politeness of all comments involved in issue resolution. Results show that higher gender diversity is linked with a lower team average issue fixing time (higher productivity), that nationality diversity is linked with lower team politeness and that gender diversity is linked with higher sentiment. We propose a computational framework for identifying linguistic aspects of politeness. Our starting point is a new corpus of requests annotated for politeness, which we use to evaluate aspects of politeness theory and to uncover new interactions between politeness markers and context. These findings guide our construction of a classifier with domain-independent lexical and syntactic features operationalizing key components of politeness theory, such as indirection, deference, impersonalization and modality. Our classifier achieves close to human performance and is effective across domains. We use our framework to study the relationship between politeness and social power, showing that polite Wikipedia editors are more likely to achieve high status through elections, but, once elevated, they become less polite. We see a similar negative correlation between politeness and power on Stack Exchange, where users at the top of the reputation scale are less polite than those at the bottom. Finally, we apply our classifier to a preliminary analysis of politeness variation by gender and community.