Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Projecting Software Defects from Analyzing Ada Designs
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
An Examination of Fault Exposure Ratio
IEEE Transactions on Software Engineering - Special issue on software reliability
Software reliability modeling survey
Handbook of software reliability engineering
Coverage measurement experience during function test
ICSE '93 Proceedings of the 15th international conference on Software Engineering
ICSE '94 Proceedings of the 16th international conference on Software engineering
Measurement and enhancement of software reliability through testing
Measurement and enhancement of software reliability through testing
A logarithmic poisson execution time model for software reliability measurement
ICSE '84 Proceedings of the 7th international conference on Software engineering
Fault exposure ratio estimation and applications
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
SREPT: Software Reliability Estimation and Prediction Tool
TOOLS '00 Proceedings of the 11th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
Requirements Volatility and Defect Density
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Module Size Distribution and Defect Density
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Security vulnerabilities in software systems: a quantitative perspective
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
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Here we investigate the underlying basis connecting the software reliability growth models to the software testing and debugging process. This is important for several reasons. First, if the parameters have an interpretation, then they constitute a metric for the software test process and the software under test. Secondly, it may be possible to estimate the parameters even before testing begins. These a priori values can serve as a check for the values computed at the beginning of testing, when the test-data is dominated by short term noise. They can also serve as initial estimates when iterative computations are used.Among the two-parameter models, the exponential model is characterized by its simplicity. Both its parameters have a simple interpretation. However, in some studies it has been found that the logarithmic poisson model has superior predictive capability. Here we present a new interpretation for the logarithmic model parameters. The problem of a priori parameter estimation is considered using actual data available. Use of the results obtained is illustrated using examples. Variability of the parameters with the testing process is examined.