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. This study aims to explore how the health of OSS projects may be assessed by practitioners from organizations’ intake perspective. 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. 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. 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.

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.

Abstract 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.

Abstract Quality requirements are vital to developing successful software products. However, there exist evidence that quality requirements are managed mostly in an “ad hoc” manner and down-prioritized. This may result in insecure, unstable, slow products, and unhappy customers. We have developed a conceptual model for the scoping process of quality requirements – QREME – and an assessment model – Q-REPM – for companies to benchmark when evaluating and improving their quality requirements practices. Our model balances an upfront forward-loop with a data-driven feedback-loop. Furthermore, it addresses both strategic and operational decisions. We have evaluated the model in a multi-case study at two companies in Sweden and three companies in The Netherlands. We assessed the scoping process practices for quality requirements and provided improvement recommendations for which practices to improve. The study confirms the existence of the constructs underlying QREME. The companies perform, in the median, 24% of the suggested actions in Q-REPM. None of the companies work data-driven with their quality requirements, even though four out of five companies could technically do so. Furthermore, on the strategic level, quality requirements practices are not systematically performed by any of the companies. The conceptual model and assessment model capture a relevant view of the quality requirements practices and offer relevant improvement proposals. However, we believe there is a need for coupling quality requirements practices to internal and external success factors to motive companies to change their ways of working. We also see improvement potential in the area of business intelligence for QREME in selecting data sources and relevant stakeholders.

Software systems are increasingly depending on data, particularly with the rising use of machine learning, and developers are looking for new sources of data. Open Data Ecosystems (ODE) is an emerging concept for data sharing under public licenses in software ecosystems, similar to Open Source Software (OSS). It has certain similarities to Open Government Data (OGD), where public agencies share data for innovation and transparency. We aimed to explore open data ecosystems involving commercial actors. Thus, we organized five focus groups with 27 practitioners from 22 companies, public organizations, and research institutes. Based on the outcomes, we surveyed three cases of emerging ODE practice to further understand the concepts and to validate the initial findings. The main outcome is an initial conceptual model of ODEs’ value, intrinsics, governance, and evolution, and propositions for practice and further research. We found that ODE must be value driven. Regarding the intrinsics of data, we found their type, meta-data, and legal frameworks influential for their openness. We also found the characteristics of ecosystem initiation, organization, data acquisition and openness be differentiating, which we advise research and practice to take into consideration.

Abstract Software engineering is decision intensive. Evidence‐based software engineering is suggested for decision‐making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open‐source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, research is needed to support decision‐making for composite software. This paper presents a roadmap for research required to support evidence‐based decision‐making for choosing and integrating assets in composite software systems. The roadmap is developed as an output from a 5‐year project in the area, including researchers from three different organizations. The roadmap is developed in an iterative process and is based on (1) systematic literature reviews of the area; (2) investigations of the state of practice, including a case survey and a survey; and (3) development and evaluation of solutions for asset identification and selection. The research activities resulted in identifying 11 areas in need of research. The areas are grouped into two categories: areas enabling evidence‐based decision‐making and those related to supporting the decision‐making. The roadmap outlines research needs in these 11 areas. The research challenges and research directions presented in this roadmap are key areas for further research to support evidence‐based decision‐making for composite software.

Data-driven software is becoming prevalent, especially with the advent of machine learning and artificial intelligence. With data-driven systems come both challenges - to keep collecting and maintaining high quality data - and opportunities - open innovation by sharing data with others. We propose Open Data Collaboration (ODC) to describe pecuniary and non-pecuniary sharing of open data, similar to Open Source Software (OSS) and in contrast to Open Government Data (OGD), where public authorities share data. To understand challenges and opportunities with ODC, we organized five focus groups with in total 27 practitioners from 22 companies, public organizations, and research institutes. In the discussions, we observed a general interest in the subject, both from private companies and public authorities. We also noticed similarities in attitudes to open innovation practices, i.e. initial resistance which gradually turned into interest. While several of the participants were experienced in open source software, no had shared data openly. Based on the findings, we identify challenges which we set out to continue addressing in future research.

Video game development is a complex endeavor, often involving complex software, large organizations, and aggressive release deadlines. Several studies have reported that periods of “crunch time” are prevalent in the video game industry, but there are few studies on the effects of time pressure. We conducted a survey with participants of the Global Game Jam (GGJ), a 48-h hackathon. Based on 198 responses, the results suggest the following: iterative brainstorming is the most popular method for conceptualizing initial requirements; continuous integration, minimum viable product, scope management, version control, and stand-up meetings are frequently applied development practices; regular communication, internal playtesting, and dynamic and proactive planning are the most common quality assurance activities; and familiarity with agile development has a weak correlation with perception of success in the GGJ. We conclude that GGJ teams rely on ad hoc approaches to the development and face-to-face communication, and recommend some complementary practices with limited overhead. Furthermore, as our findings are similar to recommendations for software startups, we posit that game jams and the startup scene share contextual similarities. Finally, we discuss the drawbacks of systemic “crunch time” and argue that game jam organizers are in a good position to problematize the phenomenon.

Software products are rarely developed from scratch and vulnerabilities in such products might reside in parts that are either open source software or provided by another organization. Hence, the total cybersecurity of a product often depends on cooperation, explicit or implicit, between several organizations. We study the attitudes and practices of companies in software ecosystems towards sharing vulnerability information. Furthermore, we compare these practices to contemporary cybersecurity recommendations. This is performed through a questionnaire-based qualitative survey. The questionnaire is divided into two parts: the providers' perspective and the acquirers' perspective. The results show that companies are willing to share information with each other regarding vulnerabilities. Sharing is not considered to be harmful neither to the cybersecurity nor their business, even though a majority of the respondents consider vulnerability information sensitive. However, the companies, despite being open to sharing, are less inclined to proactively sharing vulnerability information. Furthermore, the providers do not perceive that there is a large interest in vulnerability information from their customers. Hence, the companies' overall attitude to sharing vulnerability information is passive but open. In contrast, contemporary cybersecurity guidelines recommend active disclosure and sharing among actors in an ecosystem.

Digitalization and servitization are impacting many domains, including the mining industry. As the equipment becomes connected and technical infrastructure evolves, business models and risk management need to adapt. In this paper, we present a study on how changes in asset and risk distribution are evolving for the actors in a software ecosystem (SECO) and system-of-systems (SoS) around a mining operation. We have performed a survey to understand how Service Level Agreements (SLAs) -- a common mechanism for managing risk -- are used in other domains. Furthermore, we have performed a focus group study with companies. There is an overall trend in the mining industry to move the investment cost (CAPEX) from the mining operator to the vendors. Hence, the mining operator instead leases the equipment (as operational expense, OPEX) or even acquires a service. This change in business model impacts operation, as knowledge is moved from the mining operator to the suppliers. Furthermore, as the infrastructure becomes more complex, this implies that the mining operator is more and more reliant on the suppliers for the operation and maintenance. As this change is still in an early stage, there is no formalized risk management, e.g. through SLAs, in place. Rather, at present, the companies in the ecosystem rely more on trust and the incentives created by the promise of mutual future benefits of innovation activities. We believe there is a need to better understand how to manage risk in SECO as it is established and evolves. At the same time, in a SECO, the focus is on cooperation and innovation, the companies do not have incentives to address this unless there is an incident. Therefore, industry need, we believe, help in systematically understanding risk and defining quality aspects such as reliability and performance in the new business environment.

Data defined software is becoming more and more prevalent, especially with the advent of machine learning and artificial intelligence. With data defined systems come both challenges - to continue to collect and maintain quality data - and opportunities - open innovation by sharing with others. We propose Open Data Collaboration (ODC) to describe pecuniary and non-pecuniary sharing of open data, similar to Open Source Software. To understand challenges and opportunities with ODC, we ran focus groups with 22 companies and organizations. We observed an interest in the subject, but we conclude that the overall maturity is low and ODC is rare.

With ever-increasing productivity targets in mining operations, there is a growing interest in mining automation. In future mines, remote controlled and autonomous haulers will operate underground guided by LiDAR (Light Detection And Ranging) sensors. We envision reusing LiDAR measurements to maintain accurate mine maps that would contribute to both safety and productivity. Extrapolating from a pilot project on reliable wireless communication in Boliden's Kankberg mine, we propose establishing a System-of-Systems (SoS) with LiDAR-equipped haulers and existing mapping solutions as constituent systems. SoS requirements engineering inevitably adds a political layer, as independent actors are stakeholders both on the system and SoS levels. We present four SoS scenarios representing different business models, discussing how development and operations could be distributed among Boliden and external stakeholders, e.g., the vehicle suppliers, the hauling company, and the developers of the mapping software. Based on eight key variation points, we compare the four scenarios from both technical and business perspectives. Finally, we validate our findings in a seminar with participants from the relevant stakeholders. We conclude that to determine which scenario is the most promising for Boliden, trade-offs regarding control, costs, risks, and innovation must be carefully evaluated.

Signal-to-noise ratio (SNR) estimation is commonly used in wireless receivers to enhance the performance in different ways. In this paper a novel low complexity SNR estimator for OFDM is proposed. The estimator might be implemented using floating point representation or by using only the sign-bit, and can if desired be effectively implemented by reconfiguring the standard correlator used for time- and frequency estimation. Closed form expressions for the SNR estimate are derived for both the floating point implementation and the sign-bit implementation, and compared to simulation results both for an additive white Gaussian noise (AWGN) channel and for a frequency selective channel showing the feasibility of the proposed algorithm.

This paper presents an architecture of an autocorrelator for Orthogonal Frequency Division Multiplexing systems. The received signal is quantized to only the sign-bit, which dramatically simplifies the frequency offset estimation. Hardware cost is reduced under the assumption that synchronization during acquisition does not have to be very accurate, but sufficient for coarse estimation. The architecture is synthesized towards a 65nm low-leakage high threshold standard cell CMOS library. The proposed architecture results in area reduction of 93% if compared to typical 8-bit implementation. The area occupied by the architecture is 0.063 mm2. The architecture is evaluated for WLAN, LTE and DVB-H. Power simulations for DVB-H transmission shows a power consumption of 4.8µW per symbol.

This paper presents a reconfigurable inner receiver for the LTE, DVB-H, and IEEE802.11n (WLAN) radio systems, all of which are based on orthogonal frequency division multiplexing (OFDM). The receiver is implemented in the CAL language. An FPGA-based hardware implementation is synthesized from RTL generated from the CAL description. The purpose of our work is to investigate the feasibility of dataflow methodology for high-level description of digital radio transceivers.

This paper treats the feasibility to use only the sign bit of the in-phase and quadrature components when estimating time and frequency in OFDM systems. Using only the sign bit is shown to result in a frequency dependent bias, which can be easily compensated. The approach is evaluated for LTE and DVB-H, when the estimation is performed using the cyclic prefix, and for WLAN 802.11g, when the estimation is done using the short training field (STF). The performance is compared to a floating point implementation, and it is also compared to what is believed to be reasonable requirements for initial time and frequency estimation.

Non-functional characteristics of products can be essential for business success and are a key differentiator between a company and its competitors. This paper presents the application of a systematic, experience-based method to elicit, document, and analyze non-functional requirements. The objective of the method is to achieve a minimal and sufficient set of measurable and traceable non-functional requirements. The method gives clear guidance for the requirements elicitation, using workshops for capturing the important quality aspects and eliciting the non-functional requirements. This paper shows its application in three different settings, reporting the experience and lessons learned from industrial case studies that applied our NFR method. As the case studies were applied in different domains and performed with companies of various maturity, and since different quality attributes were considered, a set of interesting results has emerged. Therefore, each case study tells its own story about how the elicitation of NFR in industry can work. The paper discusses the different settings and gives a comparison of the different lessons we learned from the case studies.

Partitioning large high-speed globally synchronous ASICs into locally clocked blocks reduces clock skew problems and if handled correctly it also reduces the power consumption. However, to achieve these positive effects, the blocks need on-chip clock generators having properties such as small area and low power consumption. Therefore, a low power, high frequency, small area digitally controlled on-chip clock generator is designed and fabricated using a 0.35 /spl mu/m process. The clock generator delivers up to 1.15 GHz at 3.3 V supply voltage. At 1 V supply voltage, it delivers up to 92 MHz while consuming 0.16 mW.

Power consumption in clock of large high performance VLSIs can be reduced by adopting Globally Asynchronous, Locally Synchronous design style (GALS).GALS has small overheads for the global asynchronous communication and local clock generation.We propose methods to a) evaluate the benefits of GALS and account for its overheads, which can be used as the basis for partitioning the system into optimal number/size of synchronous blocks, and b) automate the synthesis of the global asynchronous communication.Three realistic ASICs, ranging in complexity from 1 to 3 million gates, were used to evaluate GALS benefits and overheads.The results show an average power saving of about 70% in clock with negligible overheads. Lowering power consumption in clock by using Globally AsynchronousLocally Synchronous design style.

To combat interference between users in a DS/CDMA system, several multi-user detection schemes have been proposed. This paper presents a prestudy for a custom DSP implementation of a multi-user detector scheme based on non-decision directed interference cancellation. Two architectural implementation methods for asynchronous detection are suggested and mutually compared. Each of the architectures is shown to have its particular advantages and therefore, a design combining the methods described in this paper is worth future studies.