Context: The increasing reliance on Open Source Software (OSS) in organizations’ software supply chains necessitates robust mechanisms in the intake process to ensure sourced components’ long-term viability and maintenance. Assessing OSS project health in the intake process is complex due to the wide range of socio-technical factors involved. Objective: This study aims to explore how the health of OSS projects may be assessed by practitioners from organizations’ intake perspective. Method: We conducted a qualitative interview survey with 17 industry experts to identify aspects and related metrics of OSS health. These were mapped against literature and two existing industry frameworks. A subset was identified and applied through a case study at a large international automotive manufacturer. Results: 21 health aspects with 72 connected metrics were identified covering community productivity and stability, orchestration, production processes, and outputs. Many metrics map against industry frameworks, while qualitative aspects are missing support. Special consideration is needed when assessing and comparing the health of OSS projects, including their life-cycle stage, complexity, governance concentration, and strategic importance for the focal organization. The case study shows that not all aspects and metrics may be leveraged due to resource constraints and complexity. Instead, subsets of metrics need to be prioritized, and applied in a structured approach using qualitative and quantitative means. Conclusions: The study provides a foundation and a starting point for developers in introducing health assessments of OSS components in their intake processes, while also pushing convergence towards a common corpus of health assessment in practice

Context: The state-of-the-art and practice on software quality is growing constantly and presents several challenges. The ever-growing body of knowledge on the topic, obfuscates the situation further, the lack of explicit structure makes it difficult to identify which properties exist today and how they can be evaluated, two critical aspects to increase software quality. Objective: A step to disambiguate software properties descriptions is made via a Property Model Ontology (PMO) and steps towards the evaluation of the structure are carried out together with experts. The objectives of this paper are: 1) present in detail the PMO, 2) describe the research process used to develop and evaluate the PMO, and, 3) exemplify the usage of the PMO through real instantiations obtained from practitioners and researchers. Method: Expert interviews and qualitative research methods are used to evaluate the PMO and develop a proof of concept. Results: The PMO consists of concepts describing extra-functional properties (EFPs) and their evaluation methods, i.e., how to measure the properties. The PMO is instantiated in a modelling environment through a metamodel and an online web-content management system. Conclusions: Consensus on the definition and structure of EFPs is achieved and a common understanding on how they can be reused in practice

During software evolution, decisions related to components’ origin or source significantly impact the quality properties of the product and development metrics such as cost, time to market, ease of maintenance, and further evolution. Thus, such decisions should ideally be supported by evidence, i.e., using previous experiences and information from different sources, even own previous experiences. A hindering factor to such reuse of previous experiences is that these decisions are highly context-dependent and it is difficult to identify when previous experiences come from sufficiently similar contexts to be useful in a current setting. Conversely, when documenting a decision (as a decision experience), it is difficult to know which context factors will be most beneficial when reusing the experience in the future. An interview study is performed to identify a list of context factors that are perceived to be most important by practitioners when using experiences to support decision-making for component sourcing, using a specific scenario with alternative sources of experiences. We observed that the further away (from a company or an interviewee) the experience evidence is, as is the case for online experiences, the more context factors are perceived as important by practitioners to make use of the experience. Furthermore, we discuss and identify further research to make this type of decision-making more evidence-based. 

Distracted driving is known to be one of the leading causes of vehicle accidents. With the increase in the number of sensors available within vehicles, there exists an abundance of data for monitoring driver behaviour, which, however, has so far only been comparable across vehicle manufacturers to a limited extent due to proprietary solutions. A special role in distraction is played by smart devices, usually used while driving, such as smartphones and smartwatches. They are repeatedly a source of distraction for drivers through calls, messages, notifications and apps usage. However, such devices can also be used for driver behaviour monitoring (like driver distraction detection), as current developments show. As vehicle manufacturer-independent devices, which are usually equipped with adequate sensor technology, they can provide significant advantages and opportunities. This work illustrates the opportunities in using smartphones and wearables to detect driver distraction. The overall architecture description of the concept, called Smart Devices Distracted Driving Detection, is presented together with a series of initial experiments of a proof-of-concept. Artificial Intelligence and more especially Machine Learning is used to assess driving distractions using smart devices in a comprehensive manner. 

Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

Quality requirements deal with how well a product should perform the intended functionality, such as start-up time and learnability. Researchers argue they are important and at the same time studies indicate there are deficiencies in practice. Our goal is to review the state of evidence for quality requirements. We want to understand the empirical research on quality requirements topics as well as evaluations of quality requirements solutions. We used a hybrid method for our systematic literature review. We defined a start set based on two literature reviews combined with a keyword-based search from selected publication venues. We snowballed based on the start set. We screened 530 papers and included 84 papers in our review. Case study method is the most common (43), followed by surveys (15) and tests (13). We found no replication studies. The two most commonly studied themes are (1) differentiating characteristics of quality requirements compared to other types of requirements, (2) the importance and prevalence of quality requirements. Quality models, QUPER, and the NFR method are evaluated in several studies, with positive indications. Goal modeling is the only modeling approach evaluated. However, all studies are small scale and long-term costs and impact are not studied. We conclude that more research is needed as empirical research on quality requirements is not increasing at the same rate as software engineering research in general. We see a gap between research and practice. The solutions proposed are usually evaluated in an academic context and surveys on quality requirements in industry indicate unsystematic handling of quality requirements.

Motivation: Society's dependence on Open Source Software (OSS) and the communities that maintain the OSS is ever-growing. So are the potential risks of, e.g., vulnerabilities being introduced in projects not actively maintained. By assessing an OSS project's capability to stay viable and maintained over time without interruption or weakening, i.e., the OSS health, users can consider the risk implied by using the OSS as is, and if necessary, decide whether to help improve the health or choose another option. However, such assessment is complex as OSS health covers a wide range of sub-topics, and existing support is limited. Aim: We aim to create an overview of characteristics that affect the health of an OSS project and enable the assessment thereof. Method: We conduct a snowball literature review based on a start set of 9 papers, and identify 146 relevant papers over two iterations of forward and backward snowballing. Health characteristics are elicited and coded using structured and axial coding into a framework structure. Results: The final framework consists of 107 health characteristics divided among 15 themes. Characteristics address the socio-technical spectrum of the community of actors maintaining the OSS project, the software and other deliverables being maintained, and the orchestration facilitating the maintenance. Characteristics are further divided based on the level of abstraction they address, i.e., the OSS project-level specifically, or the project's overarching ecosystem of related OSS projects. Conclusion: The framework provides an overview of the wide span of health characteristics that may need to be considered when evaluating OSS health and can serve as a foundation both for research and practice. © 2022 Owner/Author.

The relevance of context is particularly stressed in case studies, where it is said that “case study is an empirical method aimed at investigating contemporary phenomena in their context”. In this research, we classify context information and provide a context checklist for industrial software engineering. The checklist serves the purpose of (a) supporting researchers and practitioners in characterizing the context in which they are working; (b) supporting researchers with a checklist to identify relevant contextual information to elicit and report during primary and secondary studies. We utilized a systematic approach for constructing the classification of context information and provided a detailed definition for each item. We collected feedback from researchers as well as practitioners. The usefulness of the checklist was perceived more positively by researchers than practitioners, though they highlighted benefits (raising awareness of the importance of context and usefulness for management). The understandability was perceived positively by both practitioners and researchers. The checklist may serve as a “meta-model”, forming the basis for specific adaptations for different research areas, and as input for researchers deciding which context information to extract in systematic reviews. The checklist may also help researchers in reporting context in research papers.

Distracted driving is known to be one of the leading causes of vehicle accidents. With the increase in the number of sensors available within vehicles, there exists an abundance of data for monitoring driver behaviour, which, however, have so far only been comparable across vehicle manufacturers to a limited extent due to proprietary solutions. A special role in distraction is played by the smartphone, which is repeatedly a source of distraction for drivers through calls and messages. However, the smartphone can be used for driver behaviour monitoring (like driver distraction detection) too, as current developments show. As vehicle manufacturer-independent device, which is usually equipped with adequate sensor technology, smartphones can provide significant advantages, however, an overview of such approaches is missing so far. Thus, this work carries out an author-centric literature review of 16 research papers to illustrate the opportunities in using smartphones to detect driver distraction.