Distributed In Vivo Testing of Software Applications

Chu, Matt; Murphy, Christian; Kaiser, Gail E.

doi:10.1109/icst.2008.13

Cited by 24 publications

(22 citation statements)

References 9 publications

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“…Porting CrystalBall to detect process races is difficult because it works only with programs written in a special language, and it does checking while the deployed system is running, relying on network delay to hide the checking overhead. In-vivo testing [8] uses live program states, but it focuses on unit testing and lacks concurrency support.…”

Section: Related Workmentioning

confidence: 99%

Pervasive detection of process races in deployed systems

Laadan

Viennot

Tsai

et al. 2011

Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles

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Process races occur when multiple processes access shared operating system resources, such as files, without proper synchronization. We present the first study of real process races and the first system designed to detect them. Our study of hundreds of applications shows that process races are numerous, difficult to debug, and a real threat to reliability. To address this problem, we created RACEPRO, a system for automatically detecting these races. RACEPRO checks deployed systems in-vivo by recording live executions then deterministically replaying and checking them later. This approach increases checking coverage beyond the configurations or executions covered by software vendors or beta testing sites. RACEPRO records multiple processes, detects races in the recording among system calls that may concurrently access shared kernel objects, then tries different execution orderings of such system calls to determine which races are harmful and result in failures. To simplify race detection, RACEPRO models under-specified system calls based on load and store micro-operations. To reduce false positives and negatives, RACEPRO uses a replay and go-live mechanism to distill harmful races from benign ones. We have implemented RACEPRO in Linux, shown that it imposes only modest recording overhead, and used it to detect a number of previously unknown bugs in real applications caused by process races.

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Section: Related Workmentioning

confidence: 99%

Pervasive detection of process races in deployed systems

Laadan

Viennot

Tsai

et al. 2011

Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles

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“…In vivo testing has been introduced to perform software testing not only at the testing stage of the software development cycle, but also in the software deployment stage when the program is being executed in its operational environment (Chu et al 2008;Murphy et al 2009b). Since not all faults can be revealed using test cases in the software development stage, concurrent software testing on user input data while the software instance is running on the users' machines is an effective solution to detect more hidden faults.…”

Section: In Vivo Testing: Continuous Software Testing In An Operationmentioning

confidence: 99%

How to test bioinformatics software?

et al. 2015

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Bioinformatics is the application of computational, mathematical and statistical techniques to solve problems in biology and medicine. Bioinformatics programs developed for computational simulation and large-scale data analysis are widely used in almost all areas of biophysics. The appropriate choice of algorithms and correct implementation of these algorithms are critical for obtaining reliable computational results. Nonetheless, it is often very difficult to systematically test these programs as it is often hard to verify the correctness of the output, and to effectively generate failure-revealing test cases. Software testing is an important process of verification and validation of scientific software, but very few studies have directly dealt with the issues of bioinformatics software testing. In this work, we review important concepts and state-ofthe-art methods in the field of software testing. We also discuss recent reports on adapting and implementing software testing methodologies in the bioinformatics field, with specific examples drawn from systems biology and genomic medicine.

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“…Developers analyze the logs off-line to detect any bugs. ForkIV can also run tests over a set of deployed instances of the application [5] in order to either decrease the number of tests run per deployed instance of an application or increase the total number of tests run on an application.…”

Section: In Vivomentioning

confidence: 99%

“…Currently, providing functional transparency for online tests could incur significant overheads [5,39], since systems rely on application memory snapshotting via process forking. Our results (Section III(A)) confirm that using fork() for online testing can reduce an apache web server's maximum throughput by as much as 40%.…”

Section: Introductionmentioning

confidence: 99%