Daily questionnaire to assess self-reported well-being during a software development project

Peer reviewe

An Empirical Comparison of Dependency Issues in OSS Packaging Ecosystems

Nearly every popular programming language comes with one or more open source software packaging ecosystem(s), containing a large collection of interdependent software packages developed in that programming language. Such packaging ecosystems are extremely useful for their respective software development community. We present an empirical analysis of how the dependency graphs of three large packaging ecosystems (npm, CRAN and RubyGems) evolve over time. We study how the existing package dependencies impact the resilience of the three ecosystems over time and to which extent these ecosystems suffer from issues related to package dependency updates. We analyse specific solutions that each ecosystem has put into place and argue that none of these solutions is perfect, motivating the need for better tools to deal with package dependency update problems

Individual differences limit predicting well-being and productivity using software repositories : a longitudinal industrial study

Reports of poor work well-being and fluctuating productivity in software engineering have been reported in both academic and popular sources. Understanding and predicting these issues through repository analysis might help manage software developers' well-being. Our objective is to link data from software repositories, that is commit activity, communication, expressed sentiments, and job events, with measures of well-being obtained with a daily experience sampling questionnaire. To achieve our objective, we studied a single software project team for eight months in the software industry. Additionally, we performed semi-structured interviews to explain our results. The acquired quantitative data are analyzed with generalized linear mixed-effects models with autocorrelation structure. We find that individual variance accounts for most of the R-2 values in models predicting developers' experienced well-being and productivity. In other words, using software repository variables to predict developers' well-being or productivity is challenging due to individual differences. Prediction models developed for each developer individually work better, with fixed effects R-2 value of up to 0.24. The semi-structured interviews give insights into the well-being of software developers and the benefits of chat interaction. Our study suggests that individualized prediction models are needed for well-being and productivity prediction in software development.Peer reviewe

On the Topology of Package Dependency Networks: A Comparison of Three Programming Language Ecosystems

Package-based software ecosystems are composed of thousands of interdependent software packages. Many empirical studies have focused on software packages belonging to a single software ecosystem, and suggest to generalise the results to more ecosystems. We claim that such a generalisation is not always possible, because the technical structure of software ecosystems can be very different, even if these ecosystems belong to the same domain. We confirm this claim through a study of three big and popular package-based programming language ecosystems: R's CRAN archive network, Python's PyPI distribution, and JavaScript's NPM package manager. We study and compare the structure of their package dependency graphs and reveal some important differences that may make it difficult to generalise the findings of one ecosystem to another one

When GitHub meets CRAN: An Analysis of Inter-Repository Package Dependency Problems

When developing software packages in a software ecosystem, an important and well-known challenge is how to deal with dependencies to other packages. In presence of multiple package repositories, dependency management tends to become even more problematic. For the R ecosystem of statistical computing, dependency management is currently insufficient to deal with multiple package versions and inter- repository package dependencies. We explore how the use of GitHub influences the R ecosystem, both for the distribution of R packages and for inter-repository package dependency management. We also discuss how these problems could be addressed

A Historical Analysis of Debian Package Incompatibilities

Users and developers of software distributions are often confronted with installation problems due to conflicting packages. A prototypical example of this are the Linux distributions such as Debian. Conflicts between packages have been studied under different points of view in the literature, in particular for the Debian operating system, but little is known about how these package conflicts evolve over time. This article presents an extensive analysis of the evolution of package incompatibilities, spanning a decade of the life of the Debian stable and testing distributions for its most popular architecture, i386. Using the technique of survival analysis, this empirical study sheds some light on the origin and evolution of package incompatibilities, and provides the basis for building indicators that may be used to improve the quality of package-based distributions

An Empirical Study of Identical Function Clones in CRAN

Code clone analysis is a very active subject of study, and research on inter-project code clones is starting to emerge. In the context of software package repositories specifically, developers are confronted with the choice between depending on code implemented in other packages, or cloning this code in their own package. This article presents an empirical study of identical function clones in the CRAN package archive network, in order to understand the extent of this practice in the R community. Depending on too many packages may hamper maintainability as unexpected conflicts may arise during package updates. Duplicating functions from other packages may reduce maintainability since bug fixes or code changes are not propagated automatically to its clones. We study how the characteristics of cloned functions in CRAN snapshots evolve over time, and classify these clones depending on what has prevented package developers to rely on dependencies instead

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

A historical dataset for Gnome contributors

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