You work hard to find tools that can help you. You learn how to use and configure them. Then you find yourself working in an environment where you can't even use them. Have you encountered this frustrating situation? Danny and Alan have encountered this frustration many times before, and in this week's column, they're here to say you don't have to abandon all hope. If you're creative, you can still find tools to use–even in the most inhospitable environments.

In this study, historical inspection data from large real-time embedded systems were analyzed with the intention of improving the current review process.

The fact that you test an application extensively does not itself render the application more stable. However, NOT testing an application increases the risk that the software may not comply with the requirements and won't necessarily provide the expected business value. Here is a method for approaching risk-based testing.

No matter how well we plan and execute software development, defects are generated and can escape to the customers. Failure to quickly resolve software problems leads to negative consequences for our customers and increases internal business costs. A quick deterministic
method to prioritize problems and implement their solution helps to reduce cycle time and costs. Achieving this goal requires several steps. The first is to determine a model that links problem resolution performance to institutional variables and problem characteristics. Statistical Design of
Experiments (DOE) is a tool that provides data requirements for estimating the impacts of these variables on problem resolution. Once data has been gathered, the results of statistical analysis can be input into a mathematical optimization model to guide the organization.
This paper describes such an analysis.