TaskTracer

Dragunov, Anton N.; Dietterich, Thomas G.; Johnsrude, Kevin; McLaughlin, Matthew; Li, Lida; Herlocker, Jonathan L.

doi:10.1145/1040830.1040855

Cited by 156 publications

(21 citation statements)

References 19 publications

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“…After the participant reached a certain threshold of interaction, which we chose to be 1000 edit events 7 and no less than two weeks of activity, the participant was prompted as to whether they wanted to install the Mylar task context and task focused UI features. Installing Mylar moved a participant into the treatment phase of the study.…”

Section: Methods and Study Frameworkmentioning

confidence: 99%

“…The most directly related task management system is UMEA, which monitors user activities in a desktop environment to create "project spaces" [16]. The Task Tracer system is similar, categorizing each event with the Microsoft Windows and Office system according to a task, and using this to build up a profile for the task [7]. This profile captures the number of interactions with a resource, at the granularity of files and URLs.…”

Section: Task-centric Information Managementmentioning

confidence: 99%

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Using task context to improve programmer productivity

Kersten

Murphy

2006

Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering

372

266

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When working on a large software system, a programmer typically spends an inordinate amount of time sifting through thousands of artifacts to find just the subset of information needed to complete an assigned task. All too often, before completing the task the programmer must switch to working on a different task. These task switches waste time as the programmer must repeatedly find and identify the information relevant to the task-at-hand. In this paper, we present a mechanism that captures, models, and persists the elements and relations relevant to a task. We show how our task context model reduces information overload and focuses a programmer's work by filtering and ranking the information presented by the development environment. A task context is created by monitoring a programmer's activity and extracting the structural relationships of program artifacts. Operations on task contexts integrate with development environment features, such as structure display, search, and change management. We have validated our approach with a longitudinal field study of Mylar, our implementation of task context for the Eclipse development environment. We report a statistically significant improvement in the productivity of 16 industry programmers who voluntarily used Mylar for their daily work.

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Section: Methods and Study Frameworkmentioning

confidence: 99%

Section: Task-centric Information Managementmentioning

confidence: 99%

Using task context to improve programmer productivity

Kersten

Murphy

2006

Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering

372

266

View full text Add to dashboard Cite

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“…We applied a variation of our regularized online learning algorithms to do activity recognition in an intelligent activity management system, the TaskTracer system5,10,11.…”

Section: Application To Activity Recognitionmentioning

confidence: 99%

“…We present an application of our algorithms to an intelligent activity management system, the TaskTracer system5,10. TaskTracer helps users organize and retrieve information based on user activities.…”

Section: Introductionmentioning

confidence: 99%

A family of large margin linear classifiers and its application in dynamic environments

Shen

Dietterich

2009

Statistical Analysis

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Real-time problems, in which the learning must be fast and the importance of the features might be changing, pose a challenge to machine learning algorithms. To learn robust classifiers in such nonstationary environments, it is essential not to assign too much weight to any single feature. We solve the problems by combining regularization mechanisms with online large margin learning algorithms. We prove bounds on their error and show that removing features with small weights has little influence on the accuracy, suggesting that these methods exhibit feature selection ability. We show that such regularized learning algorithms automatically decrease the influence of the old training instances and focus on the more recent ones. This makes them especially attractive in the dynamic environments. We present experimental results on real datasets to show the merit of our algorithms.

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“…For tasks with less prescribed sequences, task models for significant variations could be created or learned and our COI model could be applied to each of them. Though this would require a large effort, it may soon be possible to develop or adapt tools to automate much of the process (e.g., see task modeling tools discussed in [12,23,35]). …”

Section: Applying the Coi Model In Practicementioning

confidence: 99%

Leveraging characteristics of task structure to predict the cost of interruption

Iqbal

Bailey

2006

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

102

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A challenge in building interruption reasoning systems is to compute an accurate cost of interruption (COI). Prior work has used non task-specific cues to predict COI, but ignore characteristics related to the structure of a task. This work investigates how well characteristics of task structure can predict COI, as objectively measured by resumption lag. In an experiment, users were interrupted during task execution at various subtask boundaries to collect a large sample of resumption lag values. Statistical methods were employed to create a parsimonious model that uses characteristics of task structure to predict COI. A subsequent experiment with different tasks showed that the model can predict COI with reasonably high accuracy. Our model can be expediently applied to many goal-directed tasks, allowing systems to make more effective decisions about when to interrupt.

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TaskTracer

Cited by 156 publications

References 19 publications

Using task context to improve programmer productivity

Using task context to improve programmer productivity

A family of large margin linear classifiers and its application in dynamic environments

Leveraging characteristics of task structure to predict the cost of interruption

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