In this paper, we present a "forward-looking" decision support framework that integrates timely metrics data with a simulation based defect model of the software development process in order to support software project management decisions regarding product quality. These predictions are evaluated using Outcome Based Control Limits (OBCLs) and Bi-Directional (both forward and reverse) simulation models of the software development process. The Bi-Directional models provide useful guidance to the program manager when evaluating possible corrective actions on a project that has gone outside the OBCLs. The forward model predicts the potential performance impacts of the proposed corrective actions. The reverse simulation models are new and take the desired system outcome as the starting point and represent the system as it evolves backward in time to identify the necessary starting point to achieve the desired outcome. The approach is presented using an illustrative example.

Software engineering (SE) truisms capture broadly-applicable principles of software construction. The trouble with truisms is that such general principles may not apply in specific cases. This paper tests the specificity of two SE truisms: (a) increasing software process level is a desirable goal; and (b) it is best to remove errors during the early parts of a software lifecycle. Our tests are based on two well-established SE models: (1) Boehm et.al.'s COCOMO II cost estimation model; and (2) Raffo's discrete event software process model of a software project life cycle. After extensive simulations of these models, the TAR2 treatment learner was applied to find the model parameters that most improved the potential performance of the real-world systems being modelled. The case studies presented here showed that these truisms are clearly sub-optimal for certain projects since other factors proved to be far more critical. Hence, we advise against truism-based process improvement. This paper offers a general alternative framework for model-based assessment of methods to improve software quality: modelling + validation + simulation + sensitivity. That is, after recording what is known in a model, that model should be validated, explored using simulations, then summarized to find the key factors that most improve model behavior.