Files in this item

FilesDescriptionFormat

application/pdf

application/pdfEffective Predi ... ausality and Atomicity.pdf (283kB)
(no description provided)PDF

Description

Title:Effective Predictive Runtime Analysis Using Sliced Causality and Atomicity
Author(s):Chen, Feng; Serbanuta, Traian Florin; Rosu, Grigore
Subject(s):computer science
Abstract:Predictive runtime analysis has been proposed to improve the effectiveness of concurrent program analysis and testing. Observing an execution, predictive runtime analysis extracts causality which is then used as the model of the program and checked against desired properties. This way, one can predict concurrent errors without actually hitting them. The causality constructed during the analysis determines the prediction ability of this approach. This paper presents an efficient and sound approach to computing sliced causality and atomicity which significantly but soundly improves existing causalities by removing irrelevant causal partial orders using dependence, relevance, and atomicity information of the program. Algorithms presented in this paper have been implemented and extensively evaluated. The results show that the technique is effective and sound: we found all the previously known bugs as well as unknown errors in popular systems, like the Tomcat webserver and the Apache FTP server, without any false alarms.
Issue Date:2007-10
Genre:Technical Report
Type:Text
URI:http://hdl.handle.net/2142/11399
Other Identifier(s):UIUCDCS-R-2007-2905
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-22


This item appears in the following Collection(s)

Item Statistics