Mobile device manufacturers face many challenges bringing quality products to market. Most testing methodologies were created for data processing, client/server, and Web products. As such, they often fail to address key areas of interest to mobile applications-usability, security, and stability. Wayne Hom discusses approaches you can use to transform requirements into usability guides and use cases into test cases to ensure maximum test coverage. He discusses automation frameworks that support multiple platforms to reduce test cycle times and increase test coverage, while measuring and reporting at the different phases of the software lifecycle. Wayne presents case studies to illustrate how to reduce test cycles by up to 75 percent. He demonstrates solutions that have helped providers of third-party applications and services manage testing cycles for multiple mobile device releases.

As testers, we focus our efforts on measuring the quality of our organization's products. We count defects and list them by severity; we compute defect density; we examine the changes in those metrics over time for trends, and we chart customer satisfaction. While these are important, Lee Copeland suggests that to reach a higher level of testing maturity, we must apply similar measurements to ourselves. He suggests you count the number of defects in your own test cases and the length of time needed to find and fix them; compute test coverage--the measure of how much of the software you have actually exercised under test conditions--and determine Defect Removal Effectiveness--the ratio of the number of defects you actually found divided by the total number you should have found. These and other metrics will help you evaluate and then improve the effectiveness and efficiency of your testing process.

Identifying defects with static analysis tools has advanced significantly in the last few years. Yet, there still are many misconceptions about the capabilities and limits of these innovative tools-and sales propaganda such as "100 percent path coverage" has not helped at all. Paul Anderson debunks common myths and clarifies the strengths and limitations of static-analysis technology. You'll learn about the types of defects that these tools can catch and the types they miss. Paul demystifies static analysis jargon, explaining terms such as "object-sensitive" and "context-sensitive". Find out how the FDA uses static analysis today to evaluate medical device software. Paul jump-starts your understanding of static analysis so you can decide where to apply this technology and have more knowledge and confidence in your interactions with tool vendors.

Conceptually, most testers and developers agree that reviews and inspections of software designs and code can improve software and reduce development costs. However, most are unaware that measuring reviews and inspections greatly magnifies these improvements and savings. Riley Rice presents data from more than 4,000 real-world software projects in different domains-defense, commercial, and government. He compares the results of three scenarios: doing few or no reviews, doing unmeasured reviews, and doing measured reviews. For each scenario, Riley compares resulting metrics: defects delivered to customers, total project pre-release costs, total project post-release costs, total project lifecycle costs, project duration, mean time between failures, and productivity. The results are surprising-measured reviews are substantially more effective-and go far beyond what most people would expect.