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Browse Prior Art Database

Easy and Accurate GUI Progress Bars

IP.com Disclosure Number: IPCOM000032463D
Original Publication Date: 2004-Nov-05
Included in the Prior Art Database: 2004-Nov-05
Document File: 2 page(s) / 41K

Publishing Venue

IBM

Abstract

Improving the accuracy of GUI Progress bars traditionally involves a lot of effort to work out the steps that the progress bar covers and also the length of duration of each step. This idea solves the problem in two ways. Firstly by having a learning phase where the progress bar learns and records how long events take scoped to a particular hardware configuration, and secondly having a feedback mechanism during standard use so that the toolbar and adjust percentage complete based on the recorded data and the how long events are currently taking.

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Easy and Accurate GUI Progress Bars

A solution is disclosed that provides an algorithm to improve the accuracy of GUI Progress Bars.

    Progress Bars are used in GUI to denote how lengthy a task is in operation and to give the user an indication of when this task will complete. The problem with Progress Bars is that they are rarely accurate and when they are a lot of programmer effort is required to make them so. Progress bars have a tendency to be jerky and % of progress really reflects the true % of overall task time that the Progress Bar is measuring. E.G. That last 10% of progress takes 25% of task elapsed time or < 1%!

    Today Progress Bars are defined with a maximum task length and are signalled with significant events in the task with a given indication of how much of the overall task is complete. This technique tends to be in-accurate.

This problem can be overcome by having a Progress Bar with a learn mode and a run mode. In learn mode, which will be used by program developers(System Testers, etc), the Progress Bar algorithm will use the signalled events to profile the task being monitored. This profiled data will be written to a permanent file store, this file will be delivered as part of any product and used in run mode to determine the true % of progress over expected elapsed time.

    The advantage of this solution is that the programmers need only define the significant events in the task. The Progress Bar algorithm will profile the task and work out how long each event takes. The more times the learn mode is used and on varying hardware systems a more accurate profile will be built up. This profile can then be used as a guide to the Progress Bar in normal run mode to accurately reflect the true task completion status. This accuracy is achieved with the significant benefit of reduced programmer development time, who only need to define events and not work out how long they take. With the profile data being amassed during System Test.

    The algorithm in detail is as follows. A Progress Bar is instantiated with a switch for Learn...