Analysis and visualization of multimodal socio-technical information of free/libre and open source software (FLOSS) Projects

Personality traits influence most, if not all, of the human activities, from those as natural as the way people walk, talk, dress and write to those most complex as the way they interact with others. Most importantly, personality influences the way people make decisions including, in the case of developers, the criteria they consider when selecting a software project they want to participate. Most of the works that study the influence of social, technical and human factors in software development projects have been focused on the impact of communications in software quality. For instance, on identifying predictors to detect files that may contain bugs before releasing an enhanced version of a software product. Only a few of these works focus on the analysis of personality traits of developers with commit permissions (committers) in Free/Libre and Open-Source Software (FLOSS) projects and their relationship with the software artifacts they interact with. This thesis presents an approach, based on the automatic recognition of personality traits from e-mails sent by committers in FLOSS projects, to uncover relationships between the social and technical aspects that occur during software development processes. Experimental results suggest the existence of some relationships among personality traits projected by the committers through their e-mails and the social (communication) and technical activities they undertake.Maestrí

Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?

Assessing the personality of software engineers may help to match individual traits with the characteristics of development activities such as code review and testing, as well as support managers in team composition. However, self-assessment questionnaires are not a practical solution for collecting multiple observations on a large scale. Instead, automatic personality detection, while overcoming these limitations, is based on off-the-shelf solutions trained on non-technical corpora, which might not be readily applicable to technical domains like software engineering. In this paper, we first assess the performance of general-purpose personality detection tools when applied to a technical corpus of developers’ emails retrieved from the public archives of the Apache Software Foundation. We observe a general low accuracy of predictions and an overall disagreement among the tools. Second, we replicate two previous research studies in software engineering by replacing the personality detection tool used to infer developers’ personalities from pull-request discussions and emails. We observe that the original results are not confirmed, i.e., changing the tool used in the original study leads to diverging conclusions. Our results suggest a need for personality detection tools specially targeted for the software engineering domain

The Risk-Taking Software Engineer: A Framed Portrait

Background: Risk-taking is prevalent in a host of activities performed by software engineers on a daily basis, yet there is scant research on it. Aims and Method: We study if software engineers' risk-taking is affected by framing effects and by software engineers' personality. To this end, we perform a survey experiment with 124 software engineers. Results: We find that framing substantially affects their risk-taking. None of the "Big Five" personality traits are related to risk-taking in software engineers after correcting for multiple testing. Conclusions: Software engineers and their managers must be aware of framing effects and account for them properly

Promises and Perils of Inferring Personality on GitHub

Personality plays a pivotal role in our understanding of human actions and behavior. Today, the applications of personality are widespread, built on the solutions from psychology to infer personality. In software engineering, for instance, one widely used solution to infer personality uses textual communication data. As studies on personality in software engineering continue to grow, it is imperative to understand the performance of these solutions. This paper compares the inferential ability of three widely studied text-based personality tests against each other and the ground truth on GitHub. We explore the challenges and potential solutions to improve the inferential ability of personality tests. Our study shows that solutions for inferring personality are far from being perfect. Software engineering communications data can infer individual developer personality with an average error rate of 41%. In the best case, the error rate can be reduced up to 36% by following our recommendations

Personality Traits of GitHub Maintainers and Their Effects on Project Success

Online collaborative environments have become important virtual workplaces for developers to work on a common problem. GitHub is an example of such environment that hosts a wealth of open source software projects. Questions such as "Who contributes to successful projects?" and "What are the characteristics of lead developers?" require further investigations. We qualitatively identify 211 maintainers in 25 maintained repositories and 23 unmaintained repositories in GitHub. We measure their Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) as the weighted sum of their Linguistic Inquiry and Word Count (LIWC) dimensions. Our results indicate that maintainers and non-maintainers are significantly different in virtually all personality traits except in Neuroticism. Maintainers in maintained repositories tend to be more open, but less extraverted and less agreeable than maintainers in unmaintained repositories. In addition to Agreeableness being a significant predictor, our analysis suggest that the success of a repository may be explained by the absolute differences in personality traits between maintainers and non-maintainers. In sum, our work aims to understand the role of a maintainer and the effects of personality traits on project success. Our findings have direct implications such that developers can be more cognizant of their behaviours, as well as their colleagues, which can result in better collaboration. By highlighting personality differences, we show that studying social and psychological constructs can be invaluable in understanding group dynamics during collaborative process

Effects of Personality Traits and Emotional Factors in Pull Request Acceptance.

Social interactions in the form of discussion are an indispensable part of collaborative software development. The discussions are essential for developers to share their views and to form a strong relationship with other teammates. These discussions invoke both positive and negative emotions such as joy, love, aggression, and disgust. Additionally, developers also exhibit hidden behaviours that dictate their personality. Some developers can be supportive and open to new ideas, whereas others can be conservative. Past research has shown that the personality of the developers has a significant role in determining the success of the task they collaboratively perform. Additionally, previous research has also shown that in online collaborative environments, the developers use signals from comments such as rudeness to determine if they are compatible to work together. Most of these studies use traditional small-scale surveys for their experiments. The transparent nature of online collaborative environments makes it easier to conduct empirical experiments by mining pull request comments. In this thesis, first, we investigate the effect of different personality traits on pull request acceptance. The results of this experiment will provide us with a valuable understanding of the personality traits of developers and help us develop tools to assist developers. We follow it with a second experiment to understand the influence of different emotional factors on pull request decisions. The emotion expressed by a developer on their teammates can be influenced by social statuses, such as the number of followers. Moreover, the teammate's team status, such as team member or outside contributor too, can influence the emotional effect. To understand moderation, we investigate different interaction effects. We start the experiment by replicating Tsay et al.'s work that examined the influence of social factors (e.g., `social distance') and technical factors (e.g., test file inclusion) for evaluating contributions. We extend their work by augmenting it with personality traits of developers and examining the influence of on the pull request evaluation process in GitHub. In particular, we extract the `Big Five' personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) of developers from their online digital footprints, such as pull request comments. We analyze the personality traits of 16,935 active developers from 1,860 projects and compare their relative importance to other non-personality factors from past research, in the pull request evaluation process. We find that pull requests from authors (requesters) who are more open and conscientious, but less extroverted, have a higher chance of approval. Furthermore, pull requests that are closed by developers (closers) who are more conscientious, extroverted, and neurotic, have a higher likelihood of acceptance. The larger the difference in personality traits between the requester and the closer, the more positive effect it has on pull request acceptance. Although the effect of personality traits is significant and comparable to technical factors, we find that social factors are still more influential when it comes to the effect in the likelihood of pull request acceptance. We perform a second experiment to analyze the effect of emotions on pull request decisions. To predict emotions in the comments, we develop a generalised, software engineering specific language model that outperforms previous machine learning algorithms on four different standard datasets. We find that the percentage of positive comments from both requester and closer has a positive association with pull request acceptance, whereas the percentage of negative comments has a negative association. Also, the polarity of the emotion associated with the first comment of both requester and closer had a positive association with pull request acceptance, i.e., more positive the emotion, the higher the likelihood of acceptance. Finally, we find that social factors moderate the effects of emotions

An Analysis of Human Aspects of Collaborative Group Members in OSS development

Open Source Software development is a collective activity that involves different software developers who may differ from each other. Although, previous researchers have focused on technical aspects like code factors, technology used etc, recently researchers have explored non-technical human aspects like personality, ethnicity and gender to measure various outcomes. This research assists the emerging state-of-the-art body on diversity research with an empirical study that analyzes how the personality and, the race and ethnic diversity of members in a collaborative group relates to their collaborative contributions in Open Source Software(OSS) development. This research contains two parts - In the first part we analyse the collaborative group members’ personalities and frequency of their collaborative contributions. In the second part we analyse the relationship between the diversity of collaborative group members’ race and ethnicity, and the frequency of their collaborative contributions in GitHub. We infer collaborative groups within a project based on the collaboration between software developers in that project. Since previous studies have shown pull requests as the major contribution for a developer to be accepted as a group member, we measure the collabora- tive contributions of the group members by the number of pull request the group members have merged collaboratively. Our results from the first part of our research, indicate that 1) the personality traits of collaborative group members does have a relationship with the frequency of their collab- orative contributions. Specifically, the more conscientious and less extroverted the group members are, the more contributions that the group members merged. Furthermore, 2) groups that are more diverse with respect to Conscientiousness or Neuroticism have a negative relationship with the frequency of their collaborative contributions. Finally, 3) collaborative groups that are having a majority of highly open, conscientious, or neurotic developers have a positive relationship with their collaborative contributions as well. Also, from the second part of our research, We observe that (1) a major part of the developer population are White developers; (2) homogeneous and heterogeneous collabo- rative groups, with respect to race and ethnicity of their group members, have a different distribution of collaborative contributions, with heterogeneous groups having more num- ber of contributions than homogeneous groups and (3) Diversity of race and ethnicity of members in a collaborative group does have a positive statistically significant relationship with the frequency of collaborative group members’ contributions

Rethinking Productivity in Software Engineering

Get the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity. The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering. Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions. What You'll Learn Review the definitions and dimensions of software productivity See how time management is having the opposite of the intended effect Develop valuable dashboards Understand the impact of sensors on productivity Avoid software development waste Work with human-centered methods to measure productivity Look at the intersection of neuroscience and productivity Manage interruptions and context-switching Who Book Is For Industry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology. ; Collects the wisdom of software engineering thought leaders in a form digestible for any developer Shares hard-won best practices and pitfalls to avoid An up to date look at current practices in software engineering productivit