In despite of agile and rapid software development (ARSD) being researched and applied extensively, managing quality requirements (QRs) are still challenging. As ARSD processes produce a large amount of data, measurement has become a strategy to facilitate QR management. This study aims to survey the literature related to QR management through metrics in ARSD, focusing on: bibliometrics, QR metrics, and quality-related indicators used in quality management. The study design includes the definition of research questions, selection criteria, and snowballing as search strategy. We selected 61 primary studies (2001–2019). Despite a large body of knowledge and standards, there is no consensus regarding QR measurement. Terminology is varying as are the measuring models. However, seemingly different measurement models do contain similarities. The industrial relevance of the primary studies shows that practitioners have a need to improve quality measurement. Our collection of measures and data sources can serve as a starting point for practitioners to include quality measurement into their decision-making processes. Researchers could benefit from the identified similarities to start building a common framework for quality measurement. In addition, this could help researchers identify what quality aspects need more focus, e.g., security and usability that have surprisingly few metrics reported.

Background: Despite the growth in the use of software analytics platforms in industry, little empirical evidence is available about the challenges that practitioners face and the value that these platforms provide. Aim: The goal of this research is to explore the benefits of using a software analytics platform for practitioners managing quality. Method: In a technology transfer project, a software analytics platform was incrementally developed between academic and industrial partners to address their software quality problems. This paper focuses on exploring the value provided by this software analytics platform in two pilot projects. Results: Practitioners emphasized major benefits including the improvement of product quality and process performance and an increased awareness of product readiness. They especially perceived the semi-automated functionality of generating quality requirements by the software analytics platform as the benefit with the highest impact and most novel value for them. Conclusions: Practitioners can benefit from modern software analytics platforms, especially if they have time to adopt such a platform carefully and integrate it into their quality assurance activities.

By 2016 an estimated 95% of all commercial software packages will include Open Source Software (OSS). This extended adoption is yet not avoiding failure rates in OSS projects to be as high as 50%. Inadequate risk management has been identified among the top mistakes to avoid when implementing OSS-based solutions. Understanding, managing and mitigating OSS adoption risks is therefore crucial to avoid potentially significant adverse impact on the business. In this position paper we portray a short report of work in progress on risk management in OSS adoption processes. We present a risk-aware technical decision-making management platform integrated in a business-oriented decision-making framework, which together support placing technical OSS adoption decisions into organizational, business strategy as well as the broader OSS community context. The platform will be validated against a collection of use cases coming from different types of organizations: big companies, SMEs, public administration, consolidated OSS communities and emergent small OSS products.