Abstract Collaborative technologies are continuously evolving to address existing problems and introduce innovative features for enhancing collaboration in the landscape of model-driven software engineering (MDSE). Different collaborative MDSE technologies (CMTs) provide different solutions to facilitate collaboration, making it hard for practitioners to choose the technology that best suits their needs. This study aims to investigate the landscape of CMTs and to provide a list of recommended technologies tailored to specific use case scenarios in the context of MDSE. We compiled a comprehensive list of CMTs using a systematic search complemented with snowballing, investigating both academic and grey literature. The technologies were selected through a set of inclusion and exclusion criteria and eventually analyzed through an in-depth analysis focusing on model management, collaboration, and communication. The findings of our study reveal that the current landscape of CMTs is characterized by a relatively narrow range of capabilities offered by different technologies. Consequently, practitioners often have to become proficient in combining several different technologies in order to meet their needs. While various CMTs offer distinct collaboration approaches, the current landscape could be richer in terms of capabilities. Our research provides a comprehensive description of recommended CMTs, enabling practitioners to make informed decisions and improve collaboration in their MDSE processes.

Background: Wrongdoings occurring within or in relation to software can have big implications on individuals, groups of people, or society as a whole. Whistleblowing is considered an effective tool to reveal and stop wrongdoing but is still a controversial topic that has been researched sparsely in the software industry. Aim: In this study we address this gap and research the current environment for whistleblowing (reporting wrongdoing) in the software industry. Method: We surveyed 147 software practitioners about their views on whistleblowing, the current means they have to report software-related wrongdoing, and the enabling and obstructing factors to whistleblow. Results: Our study shows that software practitioners have a positive view towards whistleblowing. However, in practice whistleblowing is obstructed by the difficulty of proving the actual harm and fear of retaliation. Practitioners with more years of experience report more comfort using readily established mechanisms and procedures in their organization, are more willing to speak up and have more confidence that their report will lead to action than their less experienced peers. These differences are statistically significant. Conclusion: Through our results we conclude that the software industry needs to improve the environment for whistleblowers by providing more external reporting mechanisms, anonymity, and confidentiality, as well as support practitioners with less years of experience.

From airports to banks, healthcare, space crafts, and even amazon services, technology impacts almost every aspect of today’s life. If wrongdoings occur within or in relation to technology, they can have big implications on individuals, groups of people, or society as a whole. Whistleblowers are insiders who expose such wrongdoings— eventually stopping misconducts, such as fraud, endangerment to public health and safety, or damage to the environment. Twitter is a microblogging service that allows millions of users to share their views with people distributed all over the world on a daily basis. Tweets have the potential to contain useful information about whistleblowing in tech, from the general public and whistleblowers. However, until now this point has not been researched.To fill this gap, we conducted an exploratory study on technology-related whistleblowing tweets by manually analysing tweets, utilising descriptive statistics, and machine learning techniques. We mined 7,400 tweets from whistleblowers themselves, as well as news and opinions about certain whistleblowers and whistleblowing cases. Although our results show that only 30% of the tweets in our sample dataset (obtained through specific search terms) contained relevant information about whistleblowing in technology, our analysis shows that tweets provide valuable information for both researchers and companies to understand the public opinion regarding whistleblowing cases. Furthermore, we found that machine learning techniques are promising means for extracting information about whistleblowing in tech from the vast stream of tweets.

Current software engineering practices rely on highly heterogeneous and distributed teams working in a collaborative setting. Between 2013–2020, the publication output in the field of collaborative Model-Driven Software Engineering (MDSE) has significantly increased. However, the only systematic mapping study available is limited to studies published until 2015. In this paper, we provide an update on that study for the complementing 2016–2020 period, and report the latest results, challenges, and trends. Our analysis led to selecting 29 clusters of 54 new peer-reviewed publications on collaborative MDSE. Based on the novel developments in the field, we have extended and improved the original classification framework, making it applicable to recent and future research contributions on collaborative MDSE. The insights in this paper relate to the changing trends in the field and present new relevant information.

Abstract High-tech companies are struggling today with the maintenance of legacy software. Legacy software is vital to many organizations as it contains the important business logic. To facilitate maintenance of legacy software, a comprehensive understanding of the software’s behavior is essential. In terms of component-based software engineering, it is necessary to completely understand the behavior of components in relation to their interfaces, i.e., their interface protocols, and to preserve this behavior during the maintenance activities of the components. For this purpose, we present an approach to infer the interface protocols of software components from the behavioral models of those components, learned by a blackbox technique called active (automata) learning. To validate the learned results, we applied our approach to the software components developed with model-based engineering so that equivalence can be checked between the learned models and the reference models, ensuring the behavioral relations are preserved. Experimenting with components having reference models and performing equivalence checking builds confidence that applying active learning technique to reverse engineer legacy software components, for which no reference models are available, will also yield correct results. To apply our approach in practice, we present an automated framework for conducting active learning on a large set of components and deriving their interface protocols. Using the framework, we validated our methodology by applying active learning on 202 industrial software components, out of which, interface protocols could be successfully derived for 156 components within our given time bound of 1 h for each component.

Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade-off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning). We investigate the learning time/completeness achieved trade-off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade-off we advocate extending active learning with execution logs and passive learning results. We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.