It's critical that you discover the defects before your customers do. Metrics give you plenty of data, but creating charts and graphs that properly showcase this data can be difficult. In this article, read about six techniques that can help make this task a lot easier.
Metrics are only worthwhile if you review and use them. Do your quality reports go directly from the inbox to the trash can? A quality metrics program can be a great asset to your organization. Engineering, sales, and the company overall can benefit from having such a program. This article will help you explore ways to make measurements meaningful outside of QA.
An infinite number of metrics can be applied to various aspects of software development. In fifteen years of managing software development, Mike Cohn has found a handful of metrics that really help him do his job--and keep him cool and confident when the heat is on. Here, he describes product stabilization metrics, programmer quality metrics, customer satisfaction metrics, and complexity metrics.
The six weeks of testing you've been preparing for are suddenly reduced to one, but you still want to provide some assessment of overall quality. Read about this statistical approach to predicting the number of failed test cases in an application.
Numbers count—no two ways about it. But any numbers you include in a bug report should also include the appropriate units of measure. In an example from their experience, David Wilson and Leonidas Hepis explain the importance of using consistent terminology and units of measure.
A metrics program is any planned activity in which you use measurement to meet some specific goal. If you do not have a clear technical goal for a metrics program, then you are almost certainly not ready for such a program. Here's how to design a measurement program that leads to decisions and actions.
A major challenge for software professionals interpreting data is deciding what's real and what isn't, what matters and what doesn't. A useful way to think about it is that you are trying to find the signal in the noise produced by random variation and error. Here is advice on how to extract the useful information from the "noise."
Taking development and business contexts into consideration can mean the difference between a correct assessment and a useful assessment. Here's information on how to provide an assessment that's both correct and effective.