Reconstructing Core Dumps

Röβler, Jeremias; Zeller, Andreas; Zamfir, Cristian; Candea, George

doi:10.1109/icst.2013.18

Cited by 35 publications

(18 citation statements)

References 26 publications

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“…The first category of techniques overseesby means of instrumentationthe execution of the target system on the field in order to reproduce the crashes in-house, whereas tools and approaches belonging to the second category only use data produced by the crash such as the crash stack or the core dump at crash time. In the first category, tools record different types of data such as the invoked methods [2], try-catch exceptions [15], or objects [4]. In the second category, existing tools and approaches are aimed towards understanding the causes of a crash, using data produced by the crash itself, such as a crash stack [14], previousand controlled execution [13], and so on.…”

Section: Related Workmentioning

confidence: 99%

“…In our view, JRapture fails to address the limitations caused by instrumentation because it imposes the installation of another JVM that can also monitor other software systems than the intended ones. RECORE (REconstructing CORE dumps) is a tool proposed by Robler et al The tool instruments Java byte code to wrap every method in a try-catch block while keeping a quasi-null overhead [15]. RECORE starts from the core dump and tries (with evolutionary algorithms) to reproduce the same dump by executing the subject program many time.…”

Section: On-field Record and In-house Replaymentioning

confidence: 99%

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A bug reproduction approach based on directed model checking and crash traces

Nayrolles

Hamou-Lhadj

Tahar

et al. 2016

J Software Evolu Process

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Reproducing a bug that caused a system to crash is an important task for uncovering the causes of the crash and providing appropriate fixes. In this paper, we propose a novel crash reproduction approach that combines directed model checking and backward slicing to identify the program statements needed to reproduce a crash. Our approach, named JCHARMING (Java CrasH Automatic Reproduction by directed Model checkING), uses information found in crash traces combined with static program slices to guide a model checking engine in an optimal way. We show that JCHARMING is efficient in reproducing bugs from 10 different open source systems. Overall, JCHARMING is able to reproduce 80% of the bugs used in this study in an average time of 19 min.

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Section: Related Workmentioning

confidence: 99%

Section: On-field Record and In-house Replaymentioning

confidence: 99%

A bug reproduction approach based on directed model checking and crash traces

Nayrolles

Hamou-Lhadj

Tahar

et al. 2016

J Software Evolu Process

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“…Monitoring software execution may violate privacy by collecting sensitive data, the monitoring process can be an expensive task for the large scale software and may induce a significant overhead (Chen and Kim 2015;Nayrolles et al 2017;Rößler et al 2013). Tools like ReCrash (Artzi et al 2008), ADDA (Clause and Orso 2007), Bugnet (Narayanasamy et al 2005), jRapture (Steven et al 2000), MoTiF (Gómez et al 2016), Chronicler (Bell et al 2013), and SymCrash (Cao et al 2014) fall in this category.…”

Section: Crash Reproductionmentioning

confidence: 99%

“…To help developers in this process, various automated techniques have been suggested. These techniques typically either use program runtime data (Artzi et al 2008;Clause and Orso 2007;Narayanasamy et al 2005;Steven et al 2000;Gómez et al 2016;Bell et al 2013;Cao et al 2014;Rößler et al 2013) or crash stack traces (Bianchi et al 2017;Soltani et al 2017;Nayrolles et al 2017;Xuan et al 2015;Chen and Kim 2015) to generate a test case that triggers the reported crash.…”

mentioning

confidence: 99%

“…When available, runtime data offer more information to accurately reproduce a crash. However, it also raises various concerns (for instance, privacy violation) and may induce a significant overhead during data collection (Chen and Kim 2015;Nayrolles et al 2017;Rößler et al 2013). Instead, we focus on crash reproduction based on a crash stack trace generated by a failing system.…”

mentioning

confidence: 99%

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A benchmark-based evaluation of search-based crash reproduction

et al. 2019

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Crash reproduction approaches help developers during debugging by generating a test case that reproduces a given crash. Several solutions have been proposed to automate this task. However, the proposed solutions have been evaluated on a limited number of projects, making comparison difficult. In this paper, we enhance this line of research by proposing JCrashPack, an extensible benchmark for Java crash reproduction, together with ExRunner, a tool to simply and systematically run evaluations. JCrashPack contains 200 stack traces from various Java projects, including industrial open source ones, on which we run an extensive evaluation of EvoCrash, the state-of-the-art tool for search-based crash reproduction. EvoCrash successfully reproduced 43% of the crashes. Furthermore, we observed that reproducing NullPointerException, IllegalArgumentException, and IllegalStateException is relatively easier than reproducing ClassCastException, ArrayIndexOutOfBoundsException and StringIndexOutOfBoundsException. Our results include a detailed manual analysis of EvoCrash outputs, from which we derive 14 current challenges for crash reproduction, among which the generation of input data and the handling of abstract and anonymous classes are the most frequents. Finally, based on those challenges, we discuss future research directions for search-based crash reproduction for Java.

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Search‐based crash reproduction using behavioural model seeding

Derakhshanfar

Devroey

Perrouin

et al. 2020

Software Testing Verif & Rel

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Summary Search‐based crash reproduction approaches assist developers during debugging by generating a test case, which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the search process to produce realistic sequences of method calls, which create the required objects. In this study, we introduce behavioural model seeding: a new seeding method that learns class usages from both the system under test and existing test cases. Learned usages are then synthesized in a behavioural model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioural model seeding, we evaluate it against test seeding (the state‐of‐the‐art technique for seeding realistic objects) and no seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard‐to‐reproduce crashes stemming from six open‐source projects. Our results indicate that behavioural model seeding outperforms both test seeding and no seeding by a minimum of 6% without any notable negative impact on efficiency.

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Reconstructing Core Dumps

Cited by 35 publications

References 26 publications

A bug reproduction approach based on directed model checking and crash traces

A bug reproduction approach based on directed model checking and crash traces

A benchmark-based evaluation of search-based crash reproduction

Search‐based crash reproduction using behavioural model seeding

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