Companies are interested in knowing how users experience and perceive their products. Quality of Experience (QoE) is a measurement that is used to assess the degree of delight or annoyance in experiencing a software product. To assess QoE, we have used a feedback tool integrated into a software product to ask users about their QoE ratings and to obtain information about their rationales for good or bad QoEs. It is known that requests for feedback may disturb users; however, little is known about the subjective reasoning behind this disturbance or about whether this disturbance negatively affects the QoE of the software product for which the feedback is sought. In this paper, we present a mixed qualitative-quantitative study with 35 subjects that explore the relationship between feedback requests and QoE. The subjects experienced a requirement-modeling mobile product, which was integrated with a feedback tool. During and at the end of the experience, we collected the users' perceptions of the product and the feedback requests. Based on the users' rational for being disturbed by the feedback requests, such as "early feedback," "interruptive requests," "frequent requests," and "apparently inappropriate content," we modeled feedback requests. The model defines feedback requests using a set of five-tuple variables: "task," "timing" of the task for issuing the feedback requests, user's "expertise-phase" with the product, the "frequency" of feedback requests about the task, and the "content" of the feedback request. Configuration of these parameters might drive the participants' perceived disturbances. We also found that the disturbances generated by triggering user feedback requests have negligible impacts on the QoE of software products. These results imply that software product vendors may trust users' feedback even when the feedback requests disturb the users.

To create value with a software ecosystem (SECO), a platform owner has to ensure that the SECO is healthy and sustainable. Key Performance Indicators (KPI) are used to assess whether and how well such objectives are met and what the platform owner can do to improve. This paper gives an overview of existing research on KPI-based SECO assessment using a systematic mapping of research publications. The study identified 34 relevant publications for which KPI research and KPI practice were extracted and mapped. It describes the strengths and gaps of the research published so far, and describes what KPI are measured, analyzed, and used for decision-making from the researcher's point of view. For the researcher, the maps thus capture stateof- knowledge and can be used to plan further research. For practitioners, the generated map points to studies that describe how to use KPI for managing of a SECO.

Evolution of a software product is inevitable as product context changes and the product gradually becomes less useful if it is not adapted. Planning is a basis to evolve a software product. The product manager, who carries responsibilities of planning, requires but does not always have access to high-quality information for making the best possible planning decisions. The current study aims to understand whether and when analytics are valuable for product planning and how they can be interpreted to a software product plan. The study was designed with an interview-based survey methodology approach through 17 in-depth semi-structured interviews with product managers. Based on results from qualitative analysis of the interviews, we defined an analytics-based model. The model shows that analytics have potentials to support the interpretation of product goals while is constrained by both product characteristics and product goals. The model implies how to use analytics for a good support of product planning evolution.

Quality requirements, an important class of non functional requirements, are inherently difficult to elicit. Particularly challenging is the definition of good-enough quality. The problem cannot be avoided though, because hitting the right quality level is critical. Too little quality leads to churn for the software product. Excessive quality generates unnecessary cost and drains the resources of the operating platform. To address this problem, we propose to elicit the specific relationships between software quality levels and their impacts for given quality attributes and stakeholders. An understanding of each such relationship can then be used to specify the right level of quality by deciding about acceptable impacts. The quality-impact relationships can be used to design and dimension a software system appropriately and, in a second step, to develop service level agreements that allow re-use of the obtained knowledge of good-enough quality. This paper describes an approach to elicit such quality-impact relationships and to use them for specifying quality requirements. The approach has been applied with user representatives in requirements workshops and used for determining Quality of Service (QoS) requirements based the involved users’ Quality of Experience (QoE). The paper describes the approach in detail and reports early experiences from applying the approach. Index Terms-Requirement elicitation, quality attributes, non-functional requirements, quality of experience (QoE), quality of service (QoS).

Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation. © 2018 IEEE.