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Comprehending an unfamiliar code fragment requires an awareness of explicit usage directives that may be present in the documentation of some invoked functions. Since it is not practical for developers to thoroughly investigate every call, directives may be missed and errors may occur. We previously reported on a tool called eMoose, which highlights calls to methods with associated directives, and on a controlled comparative lab study in which eMoose users were more successful at fixing bugs in given code fragments.In this paper we attempt to shed light on the factors behind these differences with a detailed analysis of videos from the study. We argue that information foraging theory may explain the subjects' reading choices and the impact of our tool. We also suggest ways to structure documentation to increase the prospects of knowledge acquisition.

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While measurements of wiki usage typically focus on the active contribution of content, information on the passive use of existing content can be valuable for a range of commercial and research purposes. In particular, such data is necessary for reconstructing the context or tracing the flow of information in settings where wikis are used as collaboration platforms in knowledge work that relies on specialized tools, such as software development.Meeting these needs requires detailed knowledge of user behavior, such as the duration for which a page was read and the sections visible at each point. This data cannot be collected by present wiki implementations and must be collected from the client-side, which presents a range of technical and privacy problems. In addition, this data must be correlated with traces of interaction with other tools.In this paper we present an approach for solving these problems in which scripts embedded by the wiki server are executed by the client browser, and report on the user's interaction with that document along with relevant structural information. These reports are relayed to a comprehensive framework for storing and accessing interaction and context data from the wiki and from additional tools used in knowledge work. This framework can be used to correlate these traces to obtain a complete view of the user's work across tools, or to approximate his context at specific points in time.