Defects
"Managing Productivity with Proper Staffing" Webinar Replay Available
Just before the holidays, we hosted our first in-house webinar, "Managing Productivity with Proper Staffing Strategies." Confronted with challenges presented by the current economy, we see more and more systems development groups trying to do more with less. The ultimate goal is to maximize productivity and minimize defects, but many teams struggle to get there. It is possible, but the most effective methods used to achieve maximum efficiency are counter-intuitive. People always think more effort will produce more product. The fact is using less effort is often more effective. Presented by industry expert, John Bryant, this webinar explains and proves the correct way to maximize productivity while at the same time minimizing cost and defects.
Calculating Mean Time to Defect
MTTD is Mean Time to Defect. Basically, it means the average time between defects (mean is the statistical term for average). A related term is MTTF, or Mean Time to Failure. It is usually meant as the average time between encountering a defect serious enough to cause the system to fail.
Is MTTD hard to compute? Does it require difficult metrics collection? Some people I have spoken to think so. Some texts think so, too. For example:
Gathering data about time between failures is very expensive. It requires recording the occurrence time of each software failure. It is sometimes quite difficult to record the time for all the failures observed during testing or operation. To be useful, time between failures data also requires a high degree of accuracy. This is perhaps the reason the MTTF metric is not widely used by commercial developers.
Finding Defects Efficiently
Several weeks ago I read an interesting study on finding bugs in giant software programs:
The efficiency of software development projects is largely determined by the way coders spot and correct errors.
But identifying bugs efficiently can be a tricky business, when the various components of a program can contain millions of lines of code. Now Michele Marchesi from the University of Calgiari and a few pals have come up with a deceptively simple way of efficiently allocating resources to error correction.
