Business process management entails a multi-billion-dollar industry that is founded on modeling business processes to analyze, understand, improve, and automate them. Business processes consist of a set of interconnected activities that an organization follows to achieve its goals and objectives. While the existence of business process models in open source has been reported in the literature, there is little work in characterizing their landscape. This paper presents the first characterization of business process models in open source, particularly on GitHub. The landscape is formed by 25,866 business process models across 4,954 repositories, with 16% of the repositories belonging to organizations. We discover that models belong to at least 16 domains including traditional software, machine learning, sales, business services, and financial services. These models are created using at least 28 different tools. Our exploration into cloning among the models shows that about 90% of all models are clones of each other. Application domains such as machine learning, traditional software, and business services demonstrate a higher occurrence of clones while in another dimension, clones are found across more repositories owned by industry as compared to those owned by academia. Also, contrary to code clones, we find that the majority of process model cloning occurs across multiple repositories. While our study acts as a precursor for future efforts to develop effective modeling practices in the field of business processes, it also emphasizes the need to address cloning and its implications in the context of reuse, maintenance, and modeling approaches.

The automotive industry is now known for its software-intensive and safety-critical nature. The industry is on a path to the holy grail of completely automating driving, starting from relatively simple operational areas like highways. One of the most challenging, evolving, and essential parts of automated driving is the software that enables understanding of surroundings and the vehicle’s own as well as surrounding objects’ relative position, otherwise known as the perception system. Current generation perception systems are formed by a combination of traditional software and machine learning-related software. With automated driving systems transitioning from research to production, it is imperative to assess their safety. We assess the safety of Apollo, the most popular open-source automotive software, at the design level for its use on a Dutch highway. We identified 58 safety requirements, 38 of which are found to be fulfilled at the design level. We observe that all requirements relating to traditional software are fulfilled, while most requirements specific to machine learning systems are not. This study unveils issues that need immediate attention; and directions for future research to make automated driving safe.

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.

Real-time systems are becoming more complex and open, thus increasing their development and verification costs. Although several static verification tools have been proposed over the last decades, they suffer from scalability and precision problems. As a result, the tools fail to cover all the necessary safety properties for realistic real-time applications involving a large number of components and tasks. Runtime verification (RV) is a formal technique that verifies properties during system execution with the support of monitors. The monitors are generated from formal languages using correct-by-construction generation methods. In this paper, we propose REVERT, a framework developed with a focus on the verification of functional and non-functional properties with timing constraints. The contribution of this work is twofold: (i) a domain-specific specification language allowing the definition of requirements for real-time applications; (ii) a novel mechanism to generate monitors, with state-space and time guarantees, capable of identifying and reacting to timing properties defined with the proposed specification language.