Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Determining an Optimal Time Interval for Testing and Debugging Software
IEEE Transactions on Software Engineering
Algorithm 611: Subroutines for Unconstrained Minimization Using a Model/Trust-Region Approach
ACM Transactions on Mathematical Software (TOMS)
Determining the Cost of a Stop-Test Decision
IEEE Software
A Decision-Analytic Stopping Rule for Validation of Commercial Software Systems
IEEE Transactions on Software Engineering
Improving reliability of large software systems
Annals of Software Engineering
SREPT: Software Reliability Estimation and Prediction Tool
TOOLS '98 Proceedings of the 10th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
Evaluation and Application of Complexity-Based Criticality Models
METRICS '96 Proceedings of the 3rd International Symposium on Software Metrics: From Measurement to Empirical Results
Reliability and Risk Analysis for Software that Must be Safe
METRICS '96 Proceedings of the 3rd International Symposium on Software Metrics: From Measurement to Empirical Results
System Test Planning of Software: An Optimization Approach
IEEE Transactions on Software Engineering
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Developers of large software systems must decide how long software should be tested before releasing it. A common and usually unwarranted assumption is that the code remains frozen during testing. We present a stochastic and economic framework to deal with systems that change as they are tested. The changes can occur because of the delivery of software as it is developed, the way software is tested, the addition of fixes, and so on. Specifically, we report the details of a real time trial of a large software system that had a substantial amount of code added during testing. We describe the methodology, give all of the relevant details, and discuss the results obtained. We pay particular attention to graphical methods that are easy to understand, and that provide effective summaries of the testing process. Some of the plots found useful by the software testers include: the Net Benefit Plot, which gives a running chart of the benefit; the Stopping Plot, which estimates the amount of additional time needed for testing; and diagnostic plots. To encourage other researchers to try out different models, all of the relevant data are provided.