Software development defect rate

The only relationship between defect levels and ranges of MTTF values reported in the literature that we are aware of are by Jones based on his empirical study several decades ago. Table Jones's data was gathered from various testing phases, from unit test to system test runs, of a systems software project. Size of the project is a key variable because it could provide crude links between defects per KLOC and total number of defects, and therefore possibly to the volume of defects and frequency of failures.

But this information was not reported. However, this relationship is very useful because it is based on empirical data on systems software. This area clearly needs more research with a large amount of empirical studies. Copyright About a decade ago, based on a sample study of U.

Based on extensive project assessments and benchmark studies, Jones estimates the typical defect rate of software organizations at SEI CMM level 1 to be 7.

For the defect rates per function point for all CMM levels, see Jones or Chapter 6 in which we discuss Jones's findings. Per IBM customers in Canada, this writer was told that the average defect rate of software in Canada a few years ago, based on a survey, was 3.

Without detailed operational definitions, it is difficult to draw meaningful conclusions on the level of defect rate or failure rate in the software industry with a certain degree of confidence. The combination of these estimates and Jones's relation between defect level and reliability, however, explains why there are so many infamous software crashes in the news.

Even though we take these estimates as "order of magnitude" estimates and allow large error margins, it is crystal clear that the level of quality for typical software is far from adequate to meet the availability requirements of businesses and safety-critical operations. Of course, this view is shared by many and has been expressed in various publications and media e. Based on our experience and assessment of available industry data, for system platforms to have high availability In other words, the defect rate has to be at or beyond the 5.

For new function development, the defect rate has to be substantially below 1 defect per thousand new and changed source instructions KCSI. This last statistic seems to correlate with Jones's finding last row in Table To achieve good product quality and high system availability, it is highly recommended that in-process reliability or outage metrics be used, and internal targets be set and achieved during the development of software.

Before the product is shipped, its field quality performance defect rate or frequency of failures should be estimated based on the in-process metrics.

Defect Distribution Pareto Chart:. You could also create a Pareto chart to find which causes will fix most defects. In many cases, a Pareto chart may not be necessary.

However, if there too many causes and the histogram or pie chart is insufficient to show the trends clearly, a Pareto chart can come in handy. Defect distribution at the end of test cycles or at a certain point in test cycles is a snapshot of defect data at that point in time. It cannot be used to derive conclusions if things are getting better or worse.

For example: At a point of time, you will know that are X number of severe bugs. We can see if defects have been increasing, decreasing or are stable over time or over releases. Defect Distribution over time by Cause. Defect Distribution over time by Module. Defect Distribution over time by Severity.

Defect Distribution over time by Platform. Plot a multiline chart for the 3 causes over 5 cycles, as below:. Bug found vs. To start creating Fixed vs. Found chart, you will have to first collect the no. This is one of the charts that need cumulative numbers to make sense. Consider the following defect data over a 10 day long test cycle:. A defect created vs.

This chart is great but there are too many lines that distract us. The raw numbers of bugs created and resolved is meaningless, you can remove them from the chart for a cleaner created vs. If the green line grew steeper and steeper it means the rate of finding the bugs has not dropped even towards the end of testing. Towards the end of the curve, the created and resolved lines are converging more or less.

This is also a good sign because it shows that the defect management process is working and is fixing the problems effectively. If the blue line is way below the green line, it means the defects are not addressed in a timely way and we might need a process improvement. Limitations: While this chart answers a lot of important questions, it does have its limitations.

Defect removal efficiency is the extent to which the development team is able to handle and remove the valid defects reported by the test team. To calculate the defect gap, get a count of total defects submitted to the Development team and the total number of defects that were fixed by the end of the cycle. Calculate a quick percentage using the formula,. Example: In a test cycle if the QA team reported defects out of which 20 were invalid not bugs, duplicates, etc.

When the data is collected over a period of time, the defect gap analysis can also be plotted as a graph as below:.

A large gap shows that the development process needs changing. Defect density is defined as the number of defects per size of the software or application area of the software. If the total number of defects at the end of a test cycle is 30 and they all originated from 6 modules, the defect density is 5.

Defect age is a measure that helps us track the average time it takes for the development team to start fixing the defect and resolve it. Defect age is usually measured in the unit days, but for teams of rapid deployment models that release weekly or daily, projects, it this should be measured in hours.

For teams with efficient development and testing processes, a low defect age signals a faster turnaround for bug fixes. Learn how Tricentis Analytics can provide portfolio visibility across testing projects and tools organization-wide.

This post was written by Swati Seela and Ryan Yackel. Test Automation. Performance Testing. Test Management. Smart Impact Analysis. Explore the platform.

Staging environments mimic the final production hosting environment of your applications. Typical defects at the staging phase include broken links, slow application performance, missing content, and any deviations from design schematics. We can pick these defects up with the use of automated functional testing tools, or even human testers. The final defect frontier is when the software is in production.

At this stage, most defects show up as performance events. You can recognize defects as user feedback that contains an application malfunction. This means developers can invest some technical effort into rectifying the defect. By now, you must be able to point a finger at specific instances and classify them as defects. That, or put a pin on them as just cases worth adding into the user manual. Keep in mind we had blinkered our focus to one software product for ease of explanation.

In reality, you may have various software projects under way. Often, this scenario leads to developers occupied by tasks from different codebases. A defect rate for all such projects would be an interesting metric as you work toward optimizing developer productivity. The simplest equation for defect rate divides the total observed defects by the number of individual projects observed. For a single product, the latter would be equal to one, leaving the rate as the number of defects detected.

Some teams focus on defects detected by users in production. Then they can trace every instance back to its root or just log a bug in a tracking system for resolution. A holistic approach to keeping track of defects is detecting them earlier in the life cycle of a software application.

At the same time, fewer defects make it to production low defect escape rate. Defect rate is a metric best kept at the minimum. The lower it is, the higher your confidence that your software is performing as expected. These actors include developers and the final users of the software they make. Discovering defects early, internally, saves your developers time having to revisit sections of code when debugging.