Monitoring the software test process using statistical process control: a logarithmic approach
Proceedings of the 9th European software engineering conference held jointly with 11th ACM SIGSOFT international symposium on Foundations of software engineering
Optimal and adaptive testing with cost constraints
Proceedings of the 2006 international workshop on Automation of software test
Software testing processes as a linear dynamic system
Information Sciences: an International Journal
On the effectiveness of early life cycle defect prediction with Bayesian Nets
Empirical Software Engineering
Requirement process establishment and improvement: from the viewpoint of cybernetics
COMPSAC-W'05 Proceedings of the 29th annual international conference on Computer software and applications conference
A software cybernetic approach to control of the software system test phase
COMPSAC-W'05 Proceedings of the 29th annual international conference on Computer software and applications conference
Computer validation oriented development of pharmaceutical automatic control software
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
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We report on the sensitivity analysis of a state variable model (Model S) proposed earlier. Model S captures the dominant behavior of the system test phase of the software test process. Sensitivity analysis is a mathematical methodology to compute changes in the system behavior due to changes in system parameters or variables. This is particularly important when parameters are calibrated using noisy or small data sets. Nevertheless, by mathematically quantifying the effects of parameter variations on the behavior of the model, and thereby the STP, one can easily and quickly evaluate the effect of such variations on the process performance without having to perform extensive simulations. In all cases studied, Model S behaved according to empirical observations which serves to validate the model. It is also shown that sensitivity analysis can suggest structural improvements in a model when the model does not behave as expected.