RESUMEN

A classical problem facing many software projects is determining when to stop testing and release the product for use. Risk analysis helps address this issue by balancing the risk exposure of doing too little with the risk exposure of doing too much. A quantitative approach based on the COCOMO II cost-estimation model and the COQUALMO quality-estimation model helps answer the question, "How much software quality investment is enough?" The authors use the models and some representative empirical data to assess the relative payoff of value-based testing as compared to value-neutral testing. They include examples of the approach's use under differing value profiles.

In most organizations, proposed investments in software dependability compete for limited resources with proposed investments in software and system functionality, response time, adaptability, speed development, ease of use, and other system capabilities. The lack of good return on-investment (ROI) models for software dependability makes determining the overall business case for dependability investments difficult. So, with a weak business case, investments in software dependability and resulting system dependability are frequently inadequate. Because different stakeholders depend on different system capabilities in different situations, the business case for dependability must deal with multiple situation-dependent attribute values. Dependability models will need to support stakeholders in determining their desired levels for each dependability attribute and estimating the cost, value, and ROI for achieving those. Those. At the University of Southern California, researchers have developed software cost-and quality-estimation models, a value-based software engineering process method, and used these models and the value-based approach to develop an Information Dependability Attribute Value Estimation model. INSET: Related Work.