Context-aware functionalities are functionalities that consider the context to produce a certain system behavior, typically an adaptation or recommendation. As contextual elements such as time, location, weather, user activity, device characteristics, network status, and countless others are becoming increasingly more accessible, the potential for adding context awareness to applications is enormous. Identifying novel, unexpected, and even delightful context-aware functionalities in practice can be challenging, though: What context information is relevant for a given user task? How can contextual elements be combined? What if there is a large number of contextual elements? Context modeling has been described in the literature as an essential aspect in the elicitation of context-aware functionalities; however, reports on the state of the practice are rare. In this study, we conducted a survey with industrial practitioners, mostly experienced professionals from large enterprises, to investigate how context models and context-modeling activities have been used to support the elicitation of context-aware functionalities. The results indicate a gap between research and industry: Context models are rarely used in practice, and context-modeling activities such as analysis of relevance and especially analysis of combinations of contextual elements have been overlooked due to their high complexity, despite practitioners recognizing their importance.

It is common practice for users to provide feedback on apps through social media or app store reviews.This feedback is a rich source of requirements for these apps.However, manually analyzing vast amounts of user feedback is unfeasible, so automated user feedback classifiers are useful tools.This research work presents a user feedback classifier based on Machine Learning (ML) for the classification of reviews according to software quality characteristics complaint with the ISO25010 standard.We developed this approach by testing several ML algorithms, features, and class balancing techniques for classifying user feedback on a data set of 1500 reviews.The maximum F1 and F2 scores obtained were 60% and 73%, with recall as high as 94%.This approach does not replace human specialists, but reduces the effort required for requirements elicitation.

We are now living in the era of digital transformation: Innovative and digital business models are transforming the global business world and society. However, the authors of this paper have perceived barriers that prevent requirements engineers from contributing properly to the development of the software systems that underpin the digital transformation. We also realized that breaking down each of these barriers would contribute to requirements engineering (RE) becoming ubiquitous in certain dimensions: RE everywhere, with everyone, for everything, automated, accepting openness, and cross-domain. In this paper, we analyze each dimension of ubiquity in the scope of the interaction between requirements engineers and end users. In particular, we point out the transformation that is required to break down each barrier, present the perspective of the scientific community and our own practical perspective, and discuss our vision on how to achieve this dimension of ubiquity. Our goal is to raise the interest of the research community in providing approaches to address the barriers and move towards ubiquitous RE.

Variability Management (VM) is a key practice in the development of variant-rich systems. Over the years, attention has been paid to VM approaches adopted by traditional software product lines. The increasing demand for dynamic and highly configurable systems, however, calls for a closer look at the approaches used to develop these systems. We therefore conducted a survey with practitioners from organizations developing variant-rich systems in order to characterize the state of the practice. We also wanted to identify factors that might influence the adoption of specific VM approaches as well as the perception of problems/difficulties posed by those. We analyzed the answers of 31 respondents from thirteen countries and found that there is a correlation between the business domain and the adopted VM approaches. With regard to the problems/difficulties, the difficulty of assuring the quality of maintenance due to the explosion of dependencies was a major issue. This paper reports on relevant findings that could help companies to better understand their problems and researchers to design new/improved solutions.

The configuration of concrete products from a product line infrastructure is the process of resolving the variability captured in the product line according to a company's market strategy or specific customer's requirements. Several aspects influence the selection of features for a concrete product, such as dependencies and constraints between features, the different stakeholders involved in the process, the desired degree of quality, and cost constraints. This paper presents the vision of a configurator that will focus on providing indicators of security and performance for features and empowering its users to interactively observe the effect of the selected set of features on these two quality characteristics. We propose the use of reusable expert knowledge and static analysis for obtaining the indicators of security and performance, respectively. The two main issues to be investigated are: (1) to which degree the configuration process should be automated; and (2) how exactly to obtain indicators of security and performance for features that can be used to predict the security and performance of whole configurations.

This workshop on services, clouds, and alternative design strategies for variant-rich software systems brings together researchers and practitioners that are interested in or have been working on variant-rich software systems.

This workshop will bring together researchers interested in raising the efficiency and the effectiveness of Software Product Line Engineering by applying innovative analysis approaches and formal methods.

Summary of Scarves workshop

Without distinguishing unstable from stable features and anticipating likely new features, building evolvability into software systems can be time-consuming and, above all, ineffective. This paper introduces a method based on a software evolution model whose goal is to help requirements engineers and product managers identify the unstable features of an embedded system and anticipate its potential adaptation needs in the future, with the aim of planning for changes beforehand. The core characteristic of this method is its support for systematic reasoning on requirements volatility and planning for changes. An application of the method in the domain of assisted living is described as a proof-of-concept, providing both a preliminary validation of the proposed solution and a useful example of its use.

Product line engineering has a widespread use in industry now. Therefore there is a high need for customizable, adaptable, and also for mature methods. Scoping and product line analysis are a unique and integral part of product line engineering. In these phases we determine where to reuse and what to reuse, establishing the basis for all technical, managerial, and investment decisions in the product line to come. Furthermore, these early phases are highly context dependant. In this tutorial we will give an introduction on how to analyze an environment with the purpose of planning a product line and its future evolution. We focus on product line requirements engineering methods, comprising product line scoping, analysis, and planning for evolution.