AutoComPaste Auto-Completing Text as an Alternative to Copy-Paste Shengdong (Shen) Zhao 1 Fanny...

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AutoComPaste Auto-Completing Text as an Alternative to Copy-Paste Shengdong (Shen) Zhao 1 Fanny Cheviler 2 Wei Tsang Ooi 1 Chee Yuan Lee 1 Arpit Agarwal 1,3 1

Transcript of AutoComPaste Auto-Completing Text as an Alternative to Copy-Paste Shengdong (Shen) Zhao 1 Fanny...

Page 1: AutoComPaste Auto-Completing Text as an Alternative to Copy-Paste Shengdong (Shen) Zhao 1 Fanny Cheviler 2 Wei Tsang Ooi 1 Chee Yuan Lee 1 Arpit Agarwal.

AutoComPasteAuto-Completing Text as an Alternative to Copy-Paste

Shengdong (Shen) Zhao 1 Fanny Cheviler 2 Wei Tsang Ooi 1 Chee Yuan Lee 1

Arpit Agarwal 1,3

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Background & Motivation

is a common computing operation

it often happens across documents

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Background & MotivationCurrent copy-paste techniques:

Ctrl-C, Ctrl-V Menu selection

Drag & drop X-Win

Chapuis and Roussel. Copy-and-paste between overlapping windows. CHI ’07

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6-Step Common Workflow

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6-Step Common Workflow

Step 1: Typing

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6-Step Common WorkflowStep 2: Context switch& Win manage

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6-Step Common Workflow Step 3:

Visual search

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6-Step Common Workflow

Step 4: Highlighting

& Copy

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6-Step Common Workflow

Step 5: Window

management

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6-Step Common Workflow

Step 6: Paste

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6-Step Common Workflow

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+ Text Unit Adjustments

Auto-Completing Text as an Alternative to Copy-Paste

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+ Text Unit Adjustments

Window management is common and tedious

Copy-paste often Interleaves typing

Copy-paste different sizes of text is common

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Logger Study• Logger that logs copy-paste event

– Automatically turned on, data send to a central server

– For each copy-paste event, we record• Type (copy | paste) • Number of windows open, host window, and

application name• Timestamp• Nearest typing event in terms of time• Content copied

– “[email protected]” is stored as “[email protected]” • Participants

– 22 students (9 female, 13 male, 21-27, M 23.14)

• Duration– 2 weeks

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Logger Study - Result• Data collected

– 34.1 MB of text data, 8168 events with 3481 (43%) copy and 4687 (57%) paste.

• Windows opened – 83% of the time, users have 6-20 concurrently

opened windows (average 12) when performing CP• Type of copy-paste

– 57% (2672) cross-document CP – 43% (2015) within-document CP

• Interleaving with typing– 42% of copy events were performed after typing, and

54% of paste events were followed by typing• Text size

– Phrases (39%), Sentences (33%), Paragraphs (28%)

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+ Text Unit Adjustments

Window management is common and tedious

Copy-paste often Interleaves typing

Copy-paste different sizes of text is common

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AutoComPaste Video

http://www.youtube.com/watch?v=KoDT3UeAoRE

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How does AutoComPaste Compare with

Traditional Copy-Paste Techniques?

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Ctrl-C, Ctrl-V Menu selection

Drag & drop X-Win

Chapuis and Roussel. Copy-and-paste between overlapping windows. CHI ’07

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What are the conditions or factors?

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1) Knowledge of content • Keyword(s) known• Keyword(s) unknown

2) Knowledge of location • Location known• Location unknown

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1) Knowledge of content • Keyword(s) known• Keyword(s) unknown

3) Visibility • Visible• Invisible

2) Knowledge of location • Location known• Location unknown

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1) Knowledge of content • Keyword(s) known• Keyword(s) unknown

3) Visibility • Visible• Invisible

4) Typing activity • Standalone• Interleaving

2) Knowledge of location • Location known• Location unknown

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1) Knowledge of content • Keyword(s) known• Keyword(s) unknown

2) Knowledge of location • Location known• Location unknown

3) Visibility • Visible• Invisible

4) Typing activity • Standalone• Interleaving

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1) Knowledge of content • Keyword(s) known• Keyword(s) unknown

2) Knowledge of location • Location known• Location unknown

3) Visibility • Visible• Invisible

4) Typing activity • Standalone• Interleaving

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1) Knowledge of content • Keyword(s) known• Keyword(s) unknown

2) Knowledge of location • Location known• Location unknown

3) Visibility • Visible• Invisible

4) Typing activity • Standalone• Interleaving

SwitchContext

WindowManagement

Homing

VisualSearch

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CP Copy-Pastebase case

S1: Content (known), Location (known), Visible (true), Typing before copy (false)

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CP Copy-Pastebase case

S1: Content (known), Location (known), Visible (true), Typing before copy (false)

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ACP AutoComPastebase caseHoming

S1: Content (known), Location (known), Visible (true), Typing before copy (false)

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ACP AutoComPastebase caseHoming

S1: Content (known), Location (known), Visible (true), Typing before copy (false)

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S1: Content (known), Location (known), Visible (true), Typing before copy (false)

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Controlled Experiment12 university participants X 2 techniques (XWin, ACP) X 2 content knowledge type (known, unknown) X 2 location knowledge type (known, unknown) X 2 visibility type (visible, invisible) X 2 pre-copy activity type (isolated, typing) X 6 trials of 3 different units of text (2 phrases + 2 sentences + 2 paragraphs)= 2304 trials total

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Results

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ACP has 29% performance

benefit

XWin has 29%

performance benefit

ACP has 140%

performance benefit

XWin has 31%

performance benefit

C(+) L(+)

C(-) L(+)

C(+) L(-)

C(-) L(-)

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Qualitative Study• 6 participants (3 female, 3 male; aged 22-

25, mean 23.8)• Realistic trip planning task

– plan a 5-day trip to Santa Barbara by gathering relevant information from 10 given webpages

– asked to include at least one outdoor activity, one indoor activity, and one restaurant for each day of the trip

• Can use either AutoComPaste and other copy-paste techniques

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ResultsAutoComPaste is heavily used and highly rated by 5/6 participants

However, one rated AutoComPaste negatively • He is a non-native English speaker

participant

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Conclusion• AutoComPaste nicely complements the

traditional copy-paste techniques– AutoComPaste has advantage when the

keyword/prefix is known– When keywords/prefix is known and location is

unknown, AutoComPaste will have the most advantage

– XWin has advantage when the keyword/prefix is unknown

• Performance of AutoComPaste is subject to typing and spelling skills

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Acknowledgment• Shi Xiaoming for programming the logger• Guia Gali and Symon Oliver for video

editing• Study participants • Members in the NUS-HCI lab• This research is supported by National

University of Singapore Academic Research Fund R-252-000-464-112

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Q & A

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Vignette (CHI ‘12)

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