Today, people find themselves surrounded by IT systems in their everyday life. Often they are not even aware that they are interacting with an IT system. More and more of these systems are context adaptive. Requirements to such systems may change for various reasons: The context may fundamentally change when other systems are introduced. New trends and fashions may evolve. Operators need to react quickly to such changes if they want to keep their systems competitive. Traditional approaches to requirements elicitation start to fail in this situation: context adaptive systems serve many users with different profiles. In addition, users may be reluctant to participate in improving it. Thus, it is hard to establish a representative model of requirements. Furthermore, it is hard to capture the context of requirements by subsequent interviews. In this paper we present a systematical approach for requirements elicitation based on observing anonymous users. The interaction of users with the system is observed in the normal working context. Observation is based on assumptions on how interaction should take place. Deviations from these assumptions point to new requirements. Observing a large number of users leads to a quantitative map of requirements in context. Preliminary evaluation shows that the approach is promising. It allows efficient observation of many stakeholders and the derivation of new requirements.
IT ecosystems are ultra-large-scale software systems that consist of various, constantly interacting and partly autonomous subsystems as well as the users of the overall system. Because of their strong integration with everyday life, these systems are often not even perceived as IT systems by its users. This is a problem for requirements engineering, as users might not know of or may not be interested in the capabilities of the system at all. This hinders the ongoing development of the system and might prevent new kinds of utilization and new business models from being realized.By introducing rules into the infrastructure of IT ecosystems that are being monitored for adherence by agents interacting in the system, deviations from these rules can be harnessed for finding potential candidates for new or changed requirements. The deviations can be processed using techniques like data mining and pattern recognition and then forwarded to requirements engineers for review. They may then leverage these implicitly expressed requirements to identify actual changes in the needs of the users of the systems, enabling further advancements of the IT ecosystem.
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