TIDY: A PBE-based framework supporting smart transformations for entity consistency in PowerPoint
Programming by Example (PBE) is increasingly assisting human users by recognizing and executing repetitive tasks, such as text editing and spreadsheet manipulation. Yet, existing work falls short on dealing with rich-formatted documents like PowerPoint (PPT) files, when examples are few and collecti...
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| Vydáno v: | Information and software technology Ročník 138; s. 106611 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.10.2021
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| Témata: | |
| ISSN: | 0950-5849, 1873-6025 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Programming by Example (PBE) is increasingly assisting human users by recognizing and executing repetitive tasks, such as text editing and spreadsheet manipulation. Yet, existing work falls short on dealing with rich-formatted documents like PowerPoint (PPT) files, when examples are few and collecting them is intrusive.
This article presents TIDY, a PBE-based framework, to assist automated entity transformations for their layout and style consistency in rich-formatted documents like PowerPoint, in a way adaptive to entity contexts and flexible with user selections.
TIDY achieves this by examining entities’ operation histories, and proposes a two-stage framework to first identify user intentions behind histories and then make wise next-operation recommendations for users, in order to maintain the entity consistency for rich-formatted documents.
We implemented TIDY as a prototype tool and integrated it into PowerPoint as a plug-in module. We experimentally evaluated TIDY with real-world user operation data. The evaluation reports that TIDY achieved promising effectiveness with a hit rate of 77.3% on average, which was stably holding for a variety of editing tasks. Besides, TIDY took only marginal time overhead, costing several to several tens of milliseconds, to complete each recommendation.
TIDY assists users to complete repetitive tasks in rich-formatted documents by non-intrusive user intention recognition and smart next-operation recommendations, which is effective and practically useful. |
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| ISSN: | 0950-5849 1873-6025 |
| DOI: | 10.1016/j.infsof.2021.106611 |