There are many metrics to measure the effectiveness of a testing team. One is the rejected defect ratio, or the number of rejected bug reports divided by the total submitted bug reports. You may think you want zero rejected bugs, but there are several reasons that’s not the case. Let's look at types of rejected bugs, see how they contribute to the rejected defect ratio, and explore the right ratio for your team.
The question of how to measure the effectiveness of testing procedures fuels heated controversies. In reality, however, testing metrics are subjective. It is recommended, therefore, that we adopt a different approach and move to measuring data and processes instead of measuring people.
With all of the advancements in defect tracking systems within the past few years, companies are still using the same ambiguous, canned fields known as Severity and Priority to categorize their defects. Let's examine a better way to assign importance to a defect.
Transforming a software development team to agile may not go as planned. The real change requires a phased approach to earn agile acceptance. That mindset must extend beyond the team to the entire organization.
What happens when defects go unnoticed until it is too late? Mayank provides an insightful view of the true cost of not providing enough test coverage during a software development lifecycle. He also suggests some techniques to ensure that defects are identified and mitigated early.
Software defect reports are among the most important deliverables to come out of software testing. They are as important as the test plan and will have more impact on the quality of the product than most other deliverables from the software test team. It's worth the effort to learn how to write an effective defect report that conveys the proper message and simplifies the process for everyone.
David Oddis talks about the importance of having an effective defect analysis process, as well as insight on how to manage testing across various SDLCs and the challenges it could present for teams. He also shares his opinions on today's hot topics.
Detection theory says: When trying to detect a certain event, a person can correctly report that it happened, miss it, report a false alarm, or correctly report that nothing happened. Under conditions of uncertainty, the decision to report an event is strongly influenced by how likely it...
As software increasingly becomes the face of the business, defects can lead to embarrassment, financial loss, and even business failure. Nevertheless, in response to today's demand for speed and “continuous everything,” the software delivery conveyer belt keeps moving faster and faster...
Can defect root cause analysis be made agile? Can we transform a multi-hour task from the classical world of software engineering into one that takes minutes and yields greater insights? Learn how Orthogonal Defect Classification (ODC) extracts semantics from defects and turns them into insights on the development process using analytics. After a quick overview of ODC, Ram Chillarege presents a case study to illustrate the method using real-world data on an agile project. They used ODC Triggers to measure test effectiveness at the end of every sprint to evaluate the effectiveness of testing compared to earlier sprints. This ODC process takes just minutes and brings its insight into the realm of the agile development practices. Put a powerful analytical technique in your agile toolbox to increase the velocity of your agile project and find new ways to reduce defects while measuring the quality of testing.
The quality problems many companies face after releasing a new product can be as painful as a root canal. One way to avoid this pain is timely root cause analysis (RCA) during development. Proper RCA and resulting improvements prevent product failures, eliminate associated rework, and reduce the pain of initial product releases. Based on empirical research conducted on today's RCA practices in the industry, Jan van Moll explains why many companies fail to do effective root cause analysis in practice. Presenting astonishing RCA data from projects, Jan shares specific examples of successes and failures with RCA. He points out the common pitfalls of defect analysis and demonstrates how to work toward problem solutions in a pragmatic and practical manner. Learn the critical success factors of RCA derived from industry experience to improve your practices and produce better products.
Jan Moll, Philips Healthcare - Magnetic Resonance Systems