All kinds of software projects, whether open or closed source, rely on open source components. Repositories that serve open source components to organizations, such as the Central Repository and npmjs.org, report billions of requests per year. Despite the widespread reliance of projects on open source components, little is known about the social interactions that occur between developers of a project using a component and developers of the component itself. In this paper, we investigate the social interactions that occur for 5,133 pairs of projects, from two different communities (Java and Ruby) representing user projects that depend on a component project. We consider such questions as how often are there social interactions when a component is used? When do the social interactions occur? And, why do social interactions occur? From our investigation, we observed that social interactions typically occur after a component has been chosen for use and relied upon. When social interactions occur, they most frequently begin with creating issues or feature requests. We also found that the more use a component receives, the less likely it is that developers of project using the component will interact with the component project, and when those interactions occur, they will be shorter in duration. Our results provide insight into how socio-technical interactions occur beyond the level of an individual or small group of projects previously studied by others and identify the need for a new model of socio-technical congruence for dependencies between, instead of within, projects.

The availability of open source software projects has created an enormous opportunity for software engineering research. However, this availability requires that researchers judiciously select an appropriate set of evaluation targets and properly document this rationale. After all, the choice of targets may have a significant effect on evaluation.

Software developers use issues as a means to describe a range of activities to be undertaken on a software system, including features to be added and defects that require fixing. When creating issues, software developers expend manual effort to specify relationships between issues, such as one issue blocking another or one issue being a sub-task of another. In particular, developers use a variety of relationships to express how work is to be broken down on a project. To better understand how software developers use work breakdown relationships between issues, we manually coded a sample of work breakdown relationships from three open source systems. We report on our findings and describe how the recognition of work breakdown relationships opens up new ways to improve software development techniques.

Knowing the impact in real settings of a software development approach is beneficial to both the industry and the research community. In this paper, we report on a field study we conducted at General Motors with the intent of understanding whether the organization was achieving some of the intended benefits of introducing a model-driven approach to software engineering. This study involved both observations and interviews. We found that several factors are still limiting the productivity increase claimed by MDE.

Turnover is the phenomenon of continuous influx and retreat of human resources in a team. Despite being well-studied in many settings, turnover has not been characterized for open-source software projects. We study the source code repositories of five open-source projects to characterize patterns of turnover and to determine the effects of turnover on software quality. We define the base concepts of both external and internal turnover, which are the mobility of developers in and out of a project, and the mobility of developers inside a project, respectively. We provide a qualitative analysis of turnover patterns. We also found, in a quantitative analysis, that the activity of external newcomers negatively impact software quality.

Context: Expert identification is becoming critical to ease the communication between developers in case of global software development or to better know members of large software communities. To quickly identify who are the experts that will best perform a given development task, both the assignment of skills to developers and the computation of their corresponding expertise level have to be automated. Since the real level of expertise is tedious to assess, our challenge is to identify developers having a significant level of experience with respect to a skill. Method: In this paper we propose XTic, an approach that takes up this challenge with the intent to be accurate and efficient. XTic provides a language to specify skills. It also provides an automatic process that extracts skills and experience levels from source code repositories. Our approach is based on the idea that an expert has a high level of experience with respect to a skill. Results: We have validated XTic both on open source and industrial projects to measure its accuracy and its efficiency. The results we obtained show that its accuracy is between moderate and strong and that it scales well with medium and large size software projects. Conclusion: XTic supports the specification of a diversity of developer skills and the extraction of the expertise of these developers under the form of level of experience.

Software bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use.

Although software development involves making numerous decisions amongst alternatives, the design and implementation choices made typically become invisible; what a developer sees in the project's artifacts are the end result of all of the decisions. What if, instead, all of the choices made were tracked and it was easy for a developer to revisit a point where a decision was made and choose another alternative? What if the development environment could detect and suggest alternative choices? What if it was easy and low-cost to try another path? We explore the idea of speculative reprogramming that could support a what-if environment for the programming stages of software development.

ABSTRACT Software intensively depends on external libraries whose relevance may change during its life cycle. As a consequence, software developers must periodically reconsider the libraries they depend on, and must think about replacing them for more relevant ones. We refer to this practice as library migration. To find the best replacement for their library, they can rely on information over the Web, but they get quickly overwhelmed by the amount of data they gather. Making the right choice in this context constitutes the topic of our work. The solution we propose is to exhibit and mine the library migrations trends computed by performing a study of a large set of software projects. To perform this analysis, we have defined an automatic approach to compute library dependencies and a semi‐automatic approach that identifies library migrations. Then, we propose a deep analysis of the library migration phenomena by performing a descriptive study of a large set of software projects stored on the Githubplatform. Second, based on our descriptive study, we propose a support to developers who want to migrate their libraries. The main result of our study is that recommendations of libraries can be inferred from the analysis of the migration trends. Copyright © 2014 John Wiley & Sons, Ltd.

In order to conduct operations research studies on complex systems, CEA/CESTA has been using and developing a new generation of simulator. The inherent complexity of such simulations ensues from the large spectrum of physical phenomena involved. As a consequence the description of a scenario and the analysis of parametric studies are time-consuming tasks. In this paper, we illustrate how the use of modeling techniques can help users construct and interpret simulation scenarios, especially users who are not computer scientists. To that end, we present a domain-specific modeling language for operations research. Moreover its associated tool, which is deployed in a large industrial context, is introduced along with model examples.