Deforestation (i.e., the removal or destruction of forests by humans), particularly illegal, is a major cause of ecological and environmental problems. To combat illegal deforestation, the World Wildlife Fund (WWF) has developed an open-source R package to predict deforestation around the world using machine learning. The package has been used by and customized to various countries, providing immense value for environmental protection. However, the package was implemented by domain experts without software engineering background, resulting in an unstructured development process, a monolithic codebase, and a lack of documentation on processes and code. Aiming to build an open-source community to improve and maintain the package, the WWF team decided to focus on enhancing the accessibility and attractiveness of the codebase for newcomers. Supporting this goal, we conducted an action-research-like project using Scrum to improve the code quality, tooling, testing, processes, and documentation while also establishing practices to sustain and build upon these improvements. In this article, we describe this project and share our insights into opening an R package to make it more accessible for external open-source contributors. Our insights include guidance on communicating design decisions to domain experts without a software engineering background and on how to train them in software engineering practices. Further insights highlight the specific challenges of working with R packages. Lastly, our work showcases the contributions that software engineering can make to support environmental protection and can guide future projects in this direction.

The automotive industry has transitioned from being an electromechanical to a software-intensive industry. A current high-end production vehicle contains 100 million+ lines of code surpassing modern airplanes, the Large Hadron Collider, the Android OS, and Facebook's front-end software, in code size by a huge margin. Today, software companies worldwide, including Apple, Google, Huawei, Baidu, and Sony are reportedly working to bring their vehicles to the road. This paper ventures into the automotive software landscape in open source, providing a first glimpse into this multi-disciplinary industry with a long history of closed source development. We paint the landscape of automotive software on GitHub by describing its characteristics and development styles.

This paper researches safety analysis for a cooperative driving system. The main objective is to assess how cooperative elements in an ISO 26262 item definition affect safety goals. The architectural model of a cooperative adaptive cruise control system is developed and its functional safety is analyzed using a combination of fault tree analysis and fault classification methods. The results show that inclusion of cooperative architecture perspective affects the safety goals of cooperative adaptive cruise control because ASIL determination is influenced by vehicle-to-vehicle communication faults.

Software fault prediction promises to be a powerful tool in supporting test engineers upon their decision where to define testing hotspots. However, there are limitations on a cross project prediction and a lack of reports upon application to industrial software, as well as the power of metrics to represent bugs. In this paper, we present a novel analysis based upon faults discovered in model-based automotive software projects and their relationship to metrics used to perform fault prediction. Using our previously released dataset on software metrics, we report bug classes discovered during heavy testing of those automotive software. As the software has been developed following strict coding and development guidelines, we present the results based on a comparison between the discovered error classes and those which might derive a reduced potential error set. Using the three projects from our dataset we determine if any of these bug classes are project specific.

In this paper, we present a novel industry dataset on static software and change metrics for Matlab/Simulink models and their corresponding auto-generated C source code. The data set comprises data of three automotive projects developed and tested accordingly to industry standards and restrictive software development guidelines. We present some background information of the projects, the development process and the issue tracking as well as the creation steps of the dataset and the used tools during development. A specific highlight of the dataset is a low measurement error on change metrics because of the used issue tracking and commit policies.

This research is part of the Hybrid Innovations for Trucks (HIT), an ongoing multi-disciplinary project with the objectives of CO$_2$ emission reduction and fuel saving for long haul vehicles. Achieving this goal necessitates definition of a proper architecture and quality techniques to enable the development of a new and more efficient control software. Therefore, this research covers automotive architecture description language and quality of automotive software.

Reverse engineering is an essential part of the modernization process that enables the evolution of existing software assets. The extraction of state machines out of existing code is an important aspect of the reverse engineering process. However, none of the reverse engineering tools fully support an automatic extraction of state machines. In our work we investigated the process of manual extraction of hierarchical state machines from the source code of an embedded C application and identified the steps of the process that can be automated. We learned that manual creation of state machines out of code is a very complicated task mostly because of the large amount of potential states that can be created by a relatively small amount of global variables. To reduce the complexity of this task we developed a methodology to decompose the code into smaller parts of functionally related elements. We showed how this technique and other system analysis mechanisms provided by the System Grokking technology can automate steps of the state machine extraction process.