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.

Crowd-based requirements engineering (CrowdRE) is promising to derive requirements by gathering and analyzing information from the crowd. Setting up CrowdRE in practice seems challenging, although first solutions to support CrowdRE exist. In this paper, we report on a German software company's experience on crowd involvement by using feedback communication channels and a monitoring solution for user-event data. In our case study, we identified several problem areas that a software company is confronted with to setup an environment for gathering requirements from the crowd. We conclude that a CrowdRE process cannot be implemented ad-hoc and that future work is needed to create and analyze a continuous feedback and monitoring data stream.

Feedback communication channels allow end-users to express their needs, which can be considered in software development and evolution. Although feedback gathering and analysis have been identified as an important topic and several researchers have started their investigation, information is scarce on how software companies currently elicit end-user feedback. In this study, we explore the experiences of software companies with respect to feedback gathering. The results of a case study and online survey indicate two sides of the same coin: on the one hand, most software companies are aware of the relevance of end-user feedback for software evolution and provide feedback channels, which allow end-users to communicate their needs and problems. On the other hand, the quantity and quality of the feedback received varies. We conclude that software companies still do not fully exploit the potential of end-user feedback for software development and evolution.

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.