Giuseppe Russo, Christoph Gote, Laurence Brandenberger, Sophia Schlosser, Frank Schweitzer. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

The tutorial will provide an introduction to the git2net, an open-source Python package for mining and analyzing collaboration in git repositories. The tutorial will cover various aspects of using git2net to analyze collaboration within git repositories, including hands-on examples and interactive tutorials using Jupyter notebooks. Attendees will learn how to use git2net to extract co-editing networks, visualize collaboration patterns, and analyze the contributions of individual developers. The tutorial is platform-independent and can be used on all platforms. The tutorial materials, including Jupyter notebooks, can be accessed through the git repository (https://github.com/gotec/git2net-tutorials). Attendees can directly interact with the notebooks through Binder, Google Colab or view them in NBViewer by following the links provided in the abstract. The tutorial will cover topics such as cloning a repository for analysis, mining git repositories with git2net, author disambiguation with gambit, network analysis with pathpy, database-based analyses, and computing file complexity with git2net. Installation instructions for git2net as well as all other information regarding its development can be found in the original development repository (https://github.com/gotec/git2net). The tutorial is suitable for developers, data scientists and researchers who are interested in understanding collaboration patterns in software development.

Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patterns in paths by which nodes can indirectly influence each other. Path data, representing ordered sequences of consecutive direct interactions, can be used to model these patterns. On the one hand, to avoid overfitting, such models should only consider those higher-order patterns for which the data provide sufficient statistical evidence. On the other hand, we hypothesise that network models, which capture only direct interactions, underfit higher-order patterns present in data. Consequently, both approaches are likely to misidentify influential nodes in complex networks. We contribute to this issue by proposing five centrality measures based on MOGen, a multi-order generative model that accounts for all indirect influences up to a maximum distance but disregards influences at higher distances. We compare MOGen-based centralities to equivalent measures for network models and path data in a prediction experiment where we aim to identify influential nodes in out-of-sample data. Our results show strong evidence supporting our hypothesis. MOGen consistently outperforms both the network model and path-based prediction. We further show that the performance difference between MOGen and the path-based approach disappears if we have sufficient observations, confirming that the error is due to overfitting.

Massive data from software repositories and collaboration tools are widely used to study social aspects in software development. One question that several recent works have addressed is how a software project's size and structure influence team productivity, a question famously considered in Brooks' law. Recent studies using massive repository data suggest that developers in larger teams tend to be less productive than smaller teams. Despite using similar methods and data, other studies argue for a positive linear or even super-linear relationship between team size and productivity, thus contesting the view of software economics that software projects are diseconomies of scale.

Name disambiguation is a complex but highly relevant challenge whenever analysing real-world user data, such as data from version control systems. We propose gambit, a rule-based disambiguation tool that only relies on name and email information. We evaluate its performance against two commonly used algorithms with similar characteristics on manually disambiguated ground-truth data from the Gnome GTK project. Our results show that gambit significantly outperforms both algorithms, achieving an F1score of 0.985.

Abstract Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication from the commit history of projects. Many works in this area studied networks ofco-authorshipof software artefacts, neglecting detailed information on code changes and code ownership available in software repositories. To address this issue, we introduce , a scalable software that facilitates the extraction of fine-grainedco-editing networksin large repositories. It uses text mining techniques to analyse the detailed history of textual modificationswithinfiles. We apply our tool in two case studies using repositories of multiple Open Source as well as a proprietary software project. Specifically, we use data on more than 1.2 million commits and more than 25,000 developers to test a hypothesis on the relation between developer productivity and co-editing patterns in software teams. We argue that opens up an important new source of high-resolution data on human collaboration patterns that can be used to advance theory in empirical software engineering, computational social science, and organisational studies.

Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication, from the commit history of projects. Most of the studied networks are based on the co-authorship of software artefacts defined at the level of files, modules, or packages. While this approach has led to insights into the social aspects of software development, it neglects detailed information on code changes and code ownership, e.g. which exact lines of code have been authored by which developers, that is contained in the commit log of software projects. Addressing this issue, we introduce git2net, a scalable python software that facilitates the extraction of fine-grained co-editing networks in large git repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. This information allows us to construct directed, weighted, and time-stamped networks, where a link signifies that one developer has edited a block of source code originally written by another developer. Our tool is applied in case studies of an Open Source and a commercial software project. We argue that it opens up a massive new source of high-resolution data on human collaboration patterns.

At higher automation levels, drivers do not need to permanently monitor the surrounding traffic environment and are allowed to focus on other tasks, while an advanced driver assistance system controls the vehicle. However, there is evidence that human drivers are prone to driving mistakes when they have to take over control from the ADAS due to their lack of situation awareness. Smoothly shifting control authority during the takeover period by a shared control approach promises to remedy this issue. We present a framework which models the human-machine interaction and makes it possible to adapt the shift of control to the needs of the human driver. Driver and ADAS are modeled as optimal controllers interfering with the same system (differential game). The handover/takeover-interaction is realized by time-variant objective functions. In consideration of the course of the takeover and the actions of the other partner this framework enables to obtain the allocation of the control effort by calculating the Nash equilibrium between driver and ADAS through solving a set of coupled Riccati differential equations. The functionality of this framework and the effects of three different takeover concepts (direct, continuous and stepwise shift) are shown via simulation of steering interaction during a lane change scenario.

To achieve a high acceptance by drivers Advanced Driver Assistance Systems (ADAS) have to consider the individual driver behavior. For example an ADAS should not intervene in a situation where drivers purposely cross road markings in harmless situations. To address this issue, a driver behavior characterization can be used to predict a driver's future trajectory based on his past behavior. In this paper, we present a method for driver behavior classification based on an inverse dynamic optimal approach and show how it can be applied to predict driver specific trajectories. The presented algorithm consists of two phases. In the trainingphase, a description of the driver's behavior is established using a set of generic driver characteristics. Hereby, a specific driver is described by his individual weighting of the characteristics and additional parameters used in the characteristics. In the prediction-phase, the model is applied to a specific track predicting the driver's future behavior. The model is adaptable to different situations and modeling purposes. It is shown by simulations that the approach is suited to model drivers with different driving characteristics and that the driver parameters can be reliably identified from recorded trajectories.