1999
DOI: 10.1145/310663.310667
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Estimation of software reliability by stratified sampling

Abstract: A new approach to software reliability estimation is presented that combines operational testing with stratified sampling in order to reduce the number of program executions that must be checked manually for conformance to requirements. Automatic cluster analysis is applied to execution profiles in order to stratify captured operational executions. Experimental results are reported that suggest this approach can significantly reduce the cost of estimating reliability.

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Cited by 60 publications
(35 citation statements)
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“…Podgurski, et al used cluster analysis of profiles and stratified random sampling to improve the accuracy of software reliability estimates [24]. Leon [5].…”
Section: Related Workmentioning
confidence: 99%
“…Podgurski, et al used cluster analysis of profiles and stratified random sampling to improve the accuracy of software reliability estimates [24]. Leon [5].…”
Section: Related Workmentioning
confidence: 99%
“…Examples of such work include that of ) who demonstrated the advantage of automated clustering of execution profiles over random selection for finding failures by using function caller/callee feature profiles as the basis for cluster formation. This work is in turn based on that of Podgurski et al (1999), who used cluster analysis of profiles and stratified random sampling to calculate estimates of software reliability and found that failures were often isolated in small clusters based on unusual execution profiles. Our work is similar to this and explores the same observed hypothesis about the distribution of failures over clusters, but we investigate the use of test case input/output pairs (and input/output pairs combined with execution profiles) from the system under test instead of execution profiles alone.…”
Section: Test Oracles Based On Anomaly Detection Techniquesmentioning
confidence: 99%
“…These clusterings can be sampled in different ways to find faults, analyse a fault in detail to come up with comprehensive fixes and so on. Earlier works have applied clustering techniques to improve several testing aspects including observation-based testing [2]- [4], [16], regression test selection [6] and test suite minimization and prioritization [17], [7].…”
Section: Related Workmentioning
confidence: 99%