Characterizing Search Behavior in Productivity Software

Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed

Moving Usability Testing onto the Web

Abstract: In order to remotely obtain detailed usability data by tracking user behaviors within a given web site, a server-based usability testing environment has been created. Web pages are annotated in such a way that arbitrary user actions (such as "mouse over link" or "click back button") can be selected for logging. In addition, the system allows the experiment designer to interleave interactive questions into the usability evaluation, which for instance could be triggered by a particular sequence of actions. The system works in conjunction with clustering and visualization algorithms that can be applied to the resulting log file data. A first version of the system has been used successfully to carry out a web usability evaluation

Web Search, Web Tutorials & Software Applications: Characterizing and Supporting the Coordinated Use of Online Resources for Performing Work in Feature-Rich Software

Web search and other online resources serve an integral role in how people learn and use feature-rich software (e.g., Adobe Photoshop) on a daily basis. Users depend on web resources both as a first line of technical support, and as a means for coping with system complexity. For example, people rely on web resources to learn new tasks, to troubleshoot problems, or to remind themselves of key task details. When users rely on web resources to support their work, their interactions are distributed over three user environments: (1) the search engine, (2) retrieved documents, and (3) the application's user interface. As users interact with these environments, their actions generate a rich set of signals that characterize how the population thinks about and uses software systems "in the wild," on a day-to-day basis. This dissertation presents three works that successively connect and associate signals and artifacts across these environments, thereby generating novel insights about users and their tasks, and enabling powerful new end-user tools and services. These three projects are as follows: Characterizing usability through search (CUTS): The CUTS system demonstrates that aggregate logs of web search queries can be leveraged to identify common tasks and potential usability problems faced by the users of any publicly available interactive system. For example, in 2011 I examined query data for the Firefox web browser. Automated analysis uncovered approximately 150 variations of the query "Firefox how to get the menu bar back", with queries issued once every 32 minutes on average. Notably, this analysis did not depend on direct access to query logs. Instead, query suggestions services and online advertising valuations were leveraged to approximate aggregate query data. Nevertheless, these data proved to be timely, to have a high degree of ecological validity, and to be arguably less prone to self-selection bias than data gathered via traditional usability methods. Query-feature graphs (QF-Graphs): Query-feature graphs are structures that map high-level descriptions of a user's goals to the specific features and commands relevant to achieving those goals in software. QF-graphs address an important instance of the more general vocabulary mismatch problem. For example, users of the GIMP photo manipulation software often want to "make a picture black and white", and fail to recognize the relevance of the applicable commands, which include: "desaturate", and "channel mixer". The key insights for building QF-graphs are that: (1) queries concisely express the user's goal in the user's own words, and (2) retrieved tutorials likely include both query terms, as well as terminology from the application's interface (e.g., the names of commands). QF-graphs are generated by mining these co-occurrences across thousands of query-tutorial pairings. InterTwine: InterTwine explores interaction possibilities that arise when software applications, web search, and online support materials are directly integrated into a single productivity system. With InterTwine, actions in the web browser directly impact how information is presented in a software application, and vice versa. For example, when a user opens a web tutorial in their browser, the application's menus and tooltips are updated to highlight the commands mentioned therein. These embellishments are designed to help users orient themselves after switching between the web browser and the application. InterTwine also augments web search results to include details of past application use. Search snippets gain before and after pictures and other metadata detailing how the user's personal work document evolved the last time they visited the page. This feature was motivated by the observation that existing mechanisms (e.g., highlighting visited links) are often insufficient for recalling which resources were previously helpful vs. unhelpful for accomplishing a task. Finally, the dissertation concludes with a discussion of the advantages, limitations and challenges of this research, and presents an outline for future work

Usability and expressiveness in database keyword search : bridging the gap

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Mining Sequences of Developer Interactions in Visual Studio for Usage Smells

In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow is resilient to the ample noise present in IDE datasets due to the mix of actions and events that these datasets typically contain. We identify usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to the understanding of developer behavior during these software development activities. Among our findings is the discovery that developers are reluctant to use conditional breakpoints when debugging, due to perceived IDE performance problems as well as due to the lack of error checking in specifying the conditional

The lifecycle of provenance metadata and its associated challenges and opportunities

This chapter outlines some of the challenges and opportunities associated with adopting provenance principles and standards in a variety of disciplines, including data publication and reuse, and information sciences

WevQuery: Testing Hypotheses about Web Interaction Patterns

Remotely stored user interaction logs, which give access to a wealth of data generated by large numbers of users, have been long used to understand if interactive systems meet the expectations of designers. Unfortunately, detailed insight into users' interaction behaviour still requires a high degree of expertise and domain specific knowledge. We present WevQuery, a scalable system to query user interaction logs in order to allow designers to test their hypotheses about users' behaviour. WevQuery supports this purpose using a graphical notation to define the interaction patterns designers are seeking. WevQuery is scalable as the queries can then be executed against large user interaction datasets by employing the MapReduce paradigm. This way WevQuery provides designers effortless access to harvest users' interaction patterns, removing the burden of low-level interaction data analysis. We present two scenarios to showcase the potential of WevQuery, from the design of the queries to their execution on real interaction data accounting for 5.7m events generated by 2,445 unique users