Relating Voluntary Turnover with Job Characteristics, Satisfaction and Work Exhaustion - An Initial Study with Brazilian Developers

High rates of turnover among software developers remain, involving additional costs of hiring and training. Voluntary turnover may be due to workplace issues or personal career decisions, but it might as well relate to Job Characteristics, or even Job Satisfaction and Work Exhaustion. This paper reports on an initial study which quantitatively measured those constructs among 78 software developers working in Brazil who left their jobs voluntarily. For this, we adapted well-known survey instruments, namely the JDS from Hackman and Oldham's Job Characteristics Model, and Maslach et al.'s Burnout Measurement. In average, developers demonstrated low to moderate autonomy (3.75, on a 1-7 scale) and satisfaction (4.08), in addition to moderate exhaustion (4.2) before leaving their jobs, while experiencing high task significance (5.15). Also, testers reported significantly lower job satisfaction than programmers. These results allow us to raise interesting hypotheses to be addressed by future studies.Comment: 4 pages, no figures, 3 tables. Final version for ICSE CHASE 201

Identifying Unmaintained Projects in GitHub

Background: Open source software has an increasing importance in modern software development. However, there is also a growing concern on the sustainability of such projects, which are usually managed by a small number of developers, frequently working as volunteers. Aims: In this paper, we propose an approach to identify GitHub projects that are not actively maintained. Our goal is to alert users about the risks of using these projects and possibly motivate other developers to assume the maintenance of the projects. Method: We train machine learning models to identify unmaintained or sparsely maintained projects, based on a set of features about project activity (commits, forks, issues, etc). We empirically validate the model with the best performance with the principal developers of 129 GitHub projects. Results: The proposed machine learning approach has a precision of 80%, based on the feedback of real open source developers; and a recall of 96%. We also show that our approach can be used to assess the risks of projects becoming unmaintained. Conclusions: The model proposed in this paper can be used by open source users and developers to identify GitHub projects that are not actively maintained anymore.Comment: Accepted at 12th International Symposium on Empirical Software Engineering and Measurement (ESEM), 10 pages, 201

The Strength of Direct Ties: Evidence from the Electronic Game Industry

We analyze the economic effects of a developer’s connectedness in the electronic game industry. Knowledge spillovers between developers should be of special relevance in this knowledge-based industry. We calculate measures for a developer’s connectedness to other developers at multiple points in time. In a regression with developer, developing firm, publishing firm, and time fixed effects, we find that the number of a developer’s direct ties, i.e., common past experience, has a strong effect on both a game’s revenues and critics’ scores. The intensity of indirect ties makes no additional contribution to the game’s success

A Research Agenda for Studying Open Source I: A Multi-Level Framework

This paper presents a research agenda for studying information systems using open source software A multi-level research model is developed at five discrete levels of analysis: (1) the artifact; (2) the individual; (3) the team, project, and community; (4) the organization; and (5) society. Each level is discussed in terms of key issues within the level. Examples are based on prior research. In a companion paper, [Niederman, et al 2006], we view the agenda through the lens of referent discipline theories

The Industry and Policy Context for Digital Games for Empowerment and Inclusion:Market Analysis, Future Prospects and Key Challenges in Videogames, Serious Games and Gamification

The effective use of digital games for empowerment and social inclusion (DGEI) of people and communities at risk of exclusion will be shaped by, and may influence the development of a range of sectors that supply products, services, technology and research. The principal industries that would appear to be implicated are the 'videogames' industry, and an emerging 'serious games' industry. The videogames industry is an ecosystem of developers, publishers and other service providers drawn from the interactive media, software and broader ICT industry that services the mainstream leisure market in games, The 'serious games' industry is a rather fragmented and growing network of firms, users, research and policy makers from a variety of sectors. This emerging industry is are trying to develop knowledge, products, services and a market for the use of digital games, and products inspired by digital games, for a range of non-leisure applications. This report provides a summary of the state of play of these industries, their trajectories and the challenges they face. It also analyses the contribution they could make to exploiting digital games for empowerment and social inclusion. Finally, it explores existing policy towards activities in these industries and markets, and draws conclusions as to the future policy relevance of engaging with them to support innovation and uptake of effective digital game-based approaches to empowerment and social inclusion.JRC.J.3-Information Societ

Should I Bug You? Identifying Domain Experts in Software Projects Using Code Complexity Metrics

In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90% of all cases