Compound-Poisson Software Reliability Model
IEEE Transactions on Software Engineering
Modeling Correlation in Software Recovery Blocks
IEEE Transactions on Software Engineering - Special issue on software reliability
Software reliability and system reliability
Handbook of software reliability engineering
Software reliability modeling survey
Handbook of software reliability engineering
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Prioritizing Test Cases For Regression Testing
IEEE Transactions on Software Engineering
Are We Testing for True Reliability?
IEEE Software
Empirical Evaluation of the Textual Differencing Regression Testing Technique
ICSM '98 Proceedings of the International Conference on Software Maintenance
ISSRE '06 Proceedings of the 17th International Symposium on Software Reliability Engineering
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The assumption of independence among successive software runs, common to many software reliability models, often is a simplification of the actual behavior. This paper addresses the problem of estimating software reliability when the successive software runs are statistically correlated, that is, when an outcome of a run depends on one or more of its previous runs. First, we propose a generalization of our previous work using higher order Markov chain to model a sequence of dependent software runs. Then, we conduct an empirical study for exploring the phenomenon of dependent software runs using three software applications as case studies. Based on two statistical approaches, we show that the outcomes of software runs (i.e., success or failure) for two of the case studies are dependent on the outcome of one or more previous runs, in which case first or higher order Markov chain models are appropriate. Finally, we estimate the parameters of the appropriate models and discuss the effects of dependent software runs on the estimates of the software reliability.