Mathieu Goeminne

On the variation and specialisation of workload--A case study of the Gnome ecosystem community

Most empirical studies of open source software repositories focus on the analysis of isolated projects, or restrict themselves to the study of the relationships between technical artifacts. In contrast, we have carried out a case study that focuses on the actual contributors to software ecosystems, being collections of software projects that are maintained by the same community. To this aim, we defined a new series of workload and involvement metrics, as well as a novel approach—

A comparison of identity merge algorithms for software repositories

\widetilde{\mathbf{T}}

A framework for analysing and visualising open source software ecosystems

-graphs—for reporting the results of comparing multiple distributions. We used these techniques to statistically study how workload and involvement of ecosystem contributors varies across projects and across activity types, and we explored to which extent projects and contributors specialise in particular activity types. Using Gnome as a case study we observed that, next to coding, the activities of localization, development documentation and building are prevalent throughout the ecosystem. We also observed notable differences between frequent and occasional contributors in terms of the activity types they are involved in and the number of projects they contribute to. Occasional contributors and contributors that are involved in many different projects tend to be more involved in the localization activity, while frequent contributors tend to be more involved in the coding activity in a limited number of projects.

Co-evolving code-related and database-related changes in a data-intensive software system

Software repository mining research extracts and analyses data originating from multiple software repositories to understand the historical development of software systems, and to propose better ways to evolve such systems in the future. Of particular interest is the study of the activities and interactions between the persons involved in the software development process. The main challenge with such studies lies in the ability to determine the identities (e.g., logins or e-mail accounts) in software repositories that represent the same physical person. To achieve this, different identity merge algorithms have been proposed in the past. This article provides an objective comparison of identity merge algorithms, including some improvements over existing algorithms. The results are validated on a selection of large ongoing open source software projects.

Towards a survival analysis of database framework usage in Java projects

Nowadays, most empirical studies in open source software evolution are based on the analysis of program code alone. In order to get a better understanding of how software evolves over time, many more entities that are part of the software ecosystem need to be taken into account. We present a general framework to automate the analysis of the evolution of software ecosystems. The framework incorporates a database that stores all relevant information obtained thanks to several mining tools, and provides a unified data source to visualisation tools. One such visualisation tool is integrated in order to get a first quick overview of the evolution of different aspects of the software project under study. The framework is extensible in order to accommodate more and different types of input and output, depending on the needs of the user. We compare our framework against existing solutions, and show how we can use this framework for carrying out concrete ecosystem evolution experiments.

SECONDA: Software Ecosystem Analysis Dashboard

Current empirical studies on the evolution of software systems are primarily analysing source code. Sometimes, social aspects such as the activity of contributors are considered as well. Very few studies, however, focus on data-intensive software systems (DISS), in which a significant part of the total development effort is devoted to maintaining and evolving the database schema. We report on early results obtained in the empirical analysis of the co-evolution between code-related and database-related activities in a large open source DISS. As a case study, we have analysed OSCAR, for which the historical information spanning many years is available in a Git repository.

A historical dataset for the Gnome ecosystem

Many software projects rely on a relational database in order to realize part of their functionality. Various database frameworks and object-relational mappings have been developed and used to facilitate data manipulation. Little is known about whether and how such frameworks co-occur, how they complement or compete with each other, and how this changes over time. We empirically studied these aspects for 5 Java database frameworks, based on a corpus of 3,707 GitHub Java projects. In particular, we analysed whether certain database frameworks co-occur frequently, and whether some database frameworks get replaced over time by others. Using the statistical technique of survival analysis, we explored the survival of the database frameworks in the considered projects. This provides useful evidence to software developers about which frameworks can be used successfully in combination and which combinations should be avoided.

Understanding the evolution of socio-technical aspects in open source ecosystems

Software ecosystems are coherent collections of software projects that evolve together and are maintained by the same developer community. Tools for analysing and visualising the evolution of software ecosystems must not only take into account the software product, but the development community as well. SECONDA is a software ecosystem visualization and analysis dashboard that offers both individual and grouped analysis of the evolution of projects and developers belonging to the software ecosystem, at coarse-grained and fine-grained level. In its current incarnation, SECONDA is used to study the GNOME ecosystem and developer community.

Analysing the evolution of social aspects of open source software ecosystems

We present a dataset of the open source software ecosystem Gnome from a social point of view. We have collected historical data about the contributors to all Gnome projects stored on git.gnome.org, taking into account the problem of identity matching, and associating different activity types to the contributors. This type of information is very useful to complement the traditional, source-code related information one can obtain by mining and analyzing the actual source code. The dataset can be obtained at https://bitbucket.org/mgoeminne/sgl-flossmetric-dbmerge.

Analyzing ecosystems for open source software developer communities: Analyzing and Managing Business Networks in the Software Industry

Open source systems being related to each other may be grouped in bigger systems called software ecosystems. The goal of our PhD dissertation [4] was to understand the evolution of the social aspects in such ecosystems. More precisely, we studied how contributors to these ecosystems can be grouped in different communities that evolve and collaborate in different ways. In doing so, we provided evidence that contributors have specificities that are not taken into account by todays analysis tools. Becoming aware of these specificities opens up new research and practically relevant questions on how new automated tools can be designed and used to offer better support to the ecosystems contributors in their activities.