February 3, 2022
When it comes to software development, mistakes become more costly to fix the later they are discovered. Any software developer, founder or CTO has a big stake in reducing that risk, and many solutions have been introduced to do just that: continuous refactoring, incremental and iterative systems like Agile, or concepts such as Simple Design.
A more universal approach, however, is simply gathering the right data and analyzing them with the right software development metrics. After all, what gets measured gets done.
Many software founders overlook the data they have on hand. Their product generates large amounts of information even while it’s still being developed. This is where production analytics comes in.
Also known as production quality analytics or production data analytics, these are “the overall measures of your software system’s performance in its current production environment.” They set a benchmark for how well your software should be running at a certain stage, and how effective your staff are at maintaining it.
The right metrics with the right analysis can bring out project bottlenecks, errors, risks or inefficiencies early in the process - preferably before you’ve started the more complex or technical tasks. That means setting up proper measurables that are aligned with your business goals, and measuring your software even while it’s still in development.
Then, as you move through your process, allot time between iterations to check if you’ve reached the benchmarks you should be at by now. If you aren’t, why not?
This metric measures how often the current application encounters failures, relative to how often it is operating. This is sometimes known as system reliability, and is calculated by noting the total amount of time it was operating and available, and dividing that by the frequency of breakdowns.
MTBF = Total Operating Time ÷ Total Number of Failures
After knowing the likelihood of downtime, measure the average time it takes for the failed component to become available again. Analyze this by dividing the total hours spent on maintenance by the frequency of repairs done.
This is useful in ensuring critical data is recovered instantly during downtime.
MTTR = Total Hours of Maintenance ÷ Total Number of Repairs
Simply put, how often does your software fail relative to how many times it is used? Unlike MTBF and MTTR, application crash rate doesn’t measure time, but frequency of the occurrence.
Crash Rate = Total Number of App Launches ÷ Total Number of App Crashes
While a zero percent failure rate is ideal, it’s nearly impossible to achieve. Watching these metrics helps you predict how probable your software is to fail, how quickly it will recover, and at what frequency these happen in production.
You can then allot time to dig out the causes of downtime and address or minimize the issues causing them, optimizing your overall product before its release and improving your product efficiency. These involve a lot of work, but the effort is worthwhile for developing a SUPERB product.
Whether you’re just exploring an ambitious product vision, or you’ve already taken your first steps, we want to help. Learn about our building blocks and how they can bring your idea to life.
