STAR: Stack Trace based Automatic Crash Reproduction

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "STAR: Stack Trace based Automatic Crash Reproduction"

By

Mr. Ning CHEN


Abstract

Software crash reproduction is a necessary first step for debugging. 
Unfortunately, crash reproduction is often difficult and labor intensive. To 
automate crash reproduction, many approaches have been proposed including 
record-replay approaches and post-failure-process approaches. Record-replay 
approaches record software executions and reliably reproduce the recorded 
executions. However, they usually incur substantial performance overhead, thus, 
not commonly deployed in practice. Alternatively, post-failure-process 
approaches perform analysis on crashes only after they have occurred. Therefore 
they do not incur performance overhead. However, existing post-failure-process 
approaches still could not reproduce many crashes in practice. In this paper, 
we propose an automatic crash reproduction framework using collected crash 
stack traces. The proposed approach combines an efficient backward symbolic 
execution and a novel method sequence composition approach to generate unit 
test cases that can reproduce the original crashes without incurring additional 
runtime overhead. Our evaluation study shows that our approach successfully 
exploited 31 (59.6%) out of 52 crashes in three open source projects. Among 
these exploitable crashes, 22 (42.3%) are useful reproductions of the original 
crashes to reveal the crash triggering bugs.


Date:			Tuesday, 5 November 2013

Time:			1:30pm – 3:30pm

Venue:			Room 3584
 			Lifts 27/28

Chairman:		Prof. Hongbin Liu (LIFS)

Committee Members:	Prof. Sunghun Kim (Supervisor)
 			Prof. Shing-Chi Cheung
 			Prof. Charles Zhang
 			Prof. James She (ECE)
                        Prof. Moonzoo Kim (KAIST, South Korea)


**** ALL are Welcome ****