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ARROW: automated repair of races on client-side web pages

Wang, Weihang ; Zheng, Yunhui ; Liu, Peng ; Xu, Lei ; Zhang, Xiangyu ; Eugster, Patrick (2016)
ARROW: automated repair of races on client-side web pages.
Saarbrücken, Germany
doi: 10.1145/2931037.2931052
Conference or Workshop Item, Bibliographie

Abstract

Modern browsers have a highly concurrent page rendering process in order to be more responsive. However, such a concurrent execution model leads to various race issues. In this paper, we present ARROW, a static technique that can automatically, safely, and cost effectively patch certain race issues on client side pages. It works by statically modeling a web page as a causal graph denoting happens-before relations between page elements, according to the rendering process in browsers. Races are detected by identifying inconsistencies between the graph and the dependence relations intended by the developer. Detected races are fixed by leveraging a constraint solver to add a set of edges with the minimum cost to the causal graph so that it is consistent with the intended dependences. The input page is then transformed to respect the repair edges. ARROW has fixed 151 races from 20 real world commercial web sites.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Wang, Weihang ; Zheng, Yunhui ; Liu, Peng ; Xu, Lei ; Zhang, Xiangyu ; Eugster, Patrick
Type of entry: Bibliographie
Title: ARROW: automated repair of races on client-side web pages
Language: German
Date: July 2016
Publisher: ACM
Issue Number: 25
Book Title: ISSTA 2016 Proceedings of the 25th International Symposium on Software Testing and Analysis
Event Location: Saarbrücken, Germany
DOI: 10.1145/2931037.2931052
Abstract:

Modern browsers have a highly concurrent page rendering process in order to be more responsive. However, such a concurrent execution model leads to various race issues. In this paper, we present ARROW, a static technique that can automatically, safely, and cost effectively patch certain race issues on client side pages. It works by statically modeling a web page as a causal graph denoting happens-before relations between page elements, according to the rendering process in browsers. Races are detected by identifying inconsistencies between the graph and the dependence relations intended by the developer. Detected races are fixed by leveraging a constraint solver to add a set of edges with the minimum cost to the causal graph so that it is consistent with the intended dependences. The input page is then transformed to respect the repair edges. ARROW has fixed 151 races from 20 real world commercial web sites.

Uncontrolled Keywords: Automatic repair, constraint solving, race condition
Identification Number: TUD-CS-2016-14778
Divisions: Profile Areas
Profile Areas > Cybersecurity (CYSEC)
Date Deposited: 14 Aug 2017 13:44
Last Modified: 12 Jan 2019 21:20
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