Quantitative Software Management (QSM) consultant, James Heires, recently discussed the benefits of estimating and forecasting software reliability at RAMS (Reliability & Maintainability Symposium) 2023.  Theme for the conference:  "Artificial Intelligence and Machine Learning (AI/ML) application to our R&M tools, techniques, and processes (and products) promises speed and scale.... When program management instantiates advanced techniques into R&M engineering activities, such as digital design and machine learning and other advanced analytics, it enables products to evolve at a much more proactive, effective, and cost-efficient approach.  Ultimately it facilitates increased speed to market, adoption of new technology, and especially for repairable systems, products that are more reliable, maintainable, and supportable."

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. 

John Bryant has over forty years of IT experience.  He spent the last several years using the SLIM Suite of Tools to improve the software development process by properly estimating, tracking, and analyzing software development efforts.  His expertise includes project management, teaching and mentoring from initial project evaluation and planning through construction. 

In case you missed it, you can view the replay of this webinar here. 

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.

But this is not really true.  The MTTD or MTTF can be computed from basic defect metrics.   All you need is:

• the total number of defects or failures and
• the total number of months, weeks, days, or hours during which the system was running or being tested and metrics were recorded.  You do not need the exact time of each defect.

Here is an example.  I will compute MTTD and MTTF two ways to demonstrate that the results are identical.  This table contains defect metrics for first three days of operation of a system that runs 24 hours a day, five days a week:

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.

...Marchesi and pals have analysed a database of java programs called Eclipse and found that the size of these programs follows a log normal distribution. In other words, the database and by extension, any large project, is made up of lots of small programs but only a few big ones.

So how are errors distributed among these programs? It would be easy to assume that the errors are evenly distributed per 1000 lines of code, regardless of the size of the program.

Not so say Marchesi and co. Their study of the Eclipse database indicates that errors are much more likely in big programs. In fact, in their study, the top 20 per cent of the largest programs contained over 60 per cent of the bugs.

That points to a clear strategy for identifying the most errors as quickly as possible in a software project: just focus on the biggest programs.

Nicole Tedesco adds her thoughts: