Organizations currently are using virtualization in the test lab to eliminate underutilized systems such as physical computers and software. So why not virtualize the costly, overutilized, or completely unavailable elements of the software architecture that have serious access and data issues for testing? These elements required for realistic end-to-end testing-mainframe computers, production systems of record, and computing services hosted by other companies-are often difficult or expensive to access for testing. Rajeev Gupta explains how virtualizing these overutilized systems can make the constraints of capacity, test data, and availability for testing a distant memory. Discover how service virtualization, employed as an adjunct to hardware lab virtualization, eliminates the bottlenecks and data management efforts that stymie many test and development teams.

For some organizations, IT governance is just another set of standards that is stated yet with few (or no) compliance expectations, little actual verification occurring, few or no metrics to indicate compliance, and even less use of the results by senior management to run their organization. Without support for standards, including practices, policy, verification, metrics and management’s commitment to use the results to manage the organization, IT governance, like any other standard, will only be perceived as yet another item that has little management support and is lacking value in the organization.

Rather than continually adding more testing, whether manual or automated, how can you assess the readiness of a software product or application for release? By extracting and analyzing the wealth of information available from existing data sources-software metrics, measures of code volatility, and historical data-you can significantly improve release decisions and overall software quality. Susan Kunz shares her experiences using these measures to decide when and when not, to release software. Susan describes how to derive quality index measures for risk, maintainability, and architectural integrity through the use of automated static and dynamic code analyses. Find out how to direct limited testing resources to error-prone code and code that really matters in a system under test. Take back new tools to make your test efforts more efficient.

• How to apply adaptive analysis to evaluate software quality

Despite the fact that iterative approaches to software development are increasingly used, most of the people paying for IT software developmet have an expectation that we should be able to tell them—before coding starts—"what's it going to do, what's it going to cost, and when's it going to be ready?" This article exlains why that's an unattainable expectation and corrects the misleading "product-lifecycle-model" for estimating.