This paper reports on TaskTracer — a software system being designed to help highly multitasking knowledge workers rapidly locate, discover, and reuse past processes they used to successfully complete tasks. The system monitors users ’ interaction with a computer, collects detailed records of users ’ activities and resources accessed, associates (automatically or with users ’ assistance) each interaction event with a particular task, enables users to access records of past activities and quickly restore task contexts. We present a novel Publisher-Subscriber architecture for collecting and processing users ’ activity data, describe several different user interfaces tried with TaskTracer, and discuss the possibility of applying machine learning techniques to recognize/predict users ’ tasks.

The continuing trend toward greater processing power, larger storage, and in particular increased display surface by using multiple monitor supports increased multi-tasking by the computer user. The concomitant increase in desktop complexity has the potential to push the overhead of window management to frustrating and counterproductive new levels. It is difficult to adequately design for multiple monitor systems without understanding how multiple monitor users differ from, or are similar to, single monitor users. Therefore, we deployed a tool to a group of single monitor and multiple monitor users to log window management activity. Analysis of the data collected from this tool revealed that usage of interaction components may change with an increase in number of monitors, and window visibility can be a useful measure of user display space management activity, especially for multiple monitor users. The results from this analysis begin to fill a gap in research about real-world window management practices.

Software products are often released with missing functionality or errors that result in failures in the eld. In previous work, we presented the Gamma technology, which facilitates remote monitoring of deployed software and allows for a prompt reaction to failures. In this paper, we investigate one of the principal technologies on which Gamma is based: software tomography. Software tomography splits monitoring tasks across many instances of the software, so that partial information can be (1) collected from users by means of light-weight instrumentation and (2) merged to gather the overall monitoring information. After describing the technology, we illustrate an instance of software tomography for a speci c monitoring task. We also present two case studies that we performed to evaluate the presented technique on a real program. The results of the studies show that software tomography can be successfully applied to collect accurate monitoring information using only minimal instrumentation on each deployed program instance.

Prototyping is the pivotal activity that structures innovation, collaboration, and creativity in design. Prototypes embody design hypotheses and enable designers to test them. Framing design as a thinking-by-doing activity foregrounds iteration as a central concern. This paper presents d.tools, a toolkit that embodies an iterative-design-centered approach to prototyping information appliances. This work offers contributions in three areas. First, d.tools introduces a statechart-based visual design tool that provides a low threshold for early-stage prototyping, extensible through code for higher-fidelity prototypes. Second, our research introduces three important types of hardware extensibility --- at the hardware-to-PC interface, the intra-hardware communication level, and the circuit level. Third, d.tools integrates design, test, and analysis of information appliances. We have evaluated d.tools through three studies: a laboratory study with thirteen participants; rebuilding prototypes of existing and emerging devices; and by observing seven student teams who built prototypes with d.tools.

Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as the data and depiction itself. Significant visualization research has focused on the generation of visualizations (the depiction); less effort has focused on the exploratory aspects of visualization (the process). However, without formal models of the process, visualization exploration sessions cannot be fully utilized to assist users and system designers. Towards this end, we introduce the P-Set Model of Visualization Exploration for describing this process and a framework to encapsulate, share, and analyze visual explorations. In addition, systems utilizing the model and framework are more efficient as redundant exploration is avoided. Several examples drawn from visualization applications demonstrate these benefits. Taken together, the model and framework provide an effective means to exploit the information within the visual exploration process.

The current state of the art in visualization research places a strong emphasis on different techniques to derive insight from disparate types of data. However, little work has investigated the visualization process itself. The information content of the visualization process—the results, history, and relationships between those results—is addressed by this work. A characterization of the visualization process is discussed, leading to a general model of the visualization exploration process. The model, based upon a new parameter derivation calculus, can be used for automated reporting, analysis, or visualized directly. An XML-based language for expressing visualization sessions using the model is also described. These sessions can then be shared and reused by collaborators. The model, along with the XML representation, provides an effective means to utilize the information within the visualization process to further data exploration.

This paper presents results of an extensive long-term clickstream study of Web browser usage. Focusing on character and challenges of page revisitation, previous findings from seven to thirteen years ago are updated. The term page revisit had to be differentiated, since the recurrence rate—the key measure for the share of page revisits—turns out to strongly depend on interpretation. We identify different types of revisitation that allow assessing the quality of current user support and developing concepts for new tools. Individual navigation strategies differ dramatically and are strongly influenced by personal habits and type of site visited. Based on user action logs and interviews, we distinguished short-term revisits (backtrack or undo) from medium-term (re-utilize or observe) and long-term revisits (rediscover). We analyze current problems and provide suggestions for improving support for different revisitation types.

Open source communities have successfully developed a great deal of software although most computer users only use proprietary applications. The usability of open source software is often regarded as one reason for this limited distribution. In this paper we review the existing evidence of the usability of open source software and discuss how the characteristics of open source development influence usability. We describe how existing human-computer interaction techniques can be used to leverage distributed networked communities, of developers and users, to address issues of usability.

There is an increasing interest in techniques that support measurement and analysis of fielded software systems. One of the main goals of these techniques is to better understand how software actually behaves in the field. In particular, many of these techniques require a way to distinguish, in the field, failing from passing executions. So far, researchers and practitioners have only partially addressed this problem: they have simply assumed that program failure status is either obvious (i.e., the program crashes) or provided by an external source (e.g., the users). In this paper, we propose a technique for automatically classifying execution data, collected in the field, as coming from either passing or failing program runs. (Failing program runs are executions that terminate with a failure, such as a wrong outcome.) We use statistical learning algorithms to build the classification models. Our approach builds the models by analyzing executions performed in a controlled environment (e.g., test cases run in-house) and then uses the models to predict whether execution data produced by a fielded instance were generated by a passing or failing program execution. We also present results from an initial feasibility study, based on multiple versions of a software subject, in which we investigate several issues vital to the applicability of the technique. Finally, we present some lessons learned regarding the interplay between the reliability of classification models and the amount and type of data collected.

It is difficult to fully assess the quality of software inhouse, outside the actual time and context in which it will execute after deployment. As a result, it is common for software to manifest field failures, failures that occur on user machines due to untested behavior. Field failures are typically difficult to recreate and investigate on developer platforms, and existing techniques based on crash reporting provide only limited support for this task. In this paper, we present a technique for recording, reproducing, and minimizing failing executions that enables and supports inhouse debugging of field failures. We also present a tool that implements our technique and an empirical study that evaluates the technique on a widely used e-mail client.