Software quality kpis

From ensuring the accuracy of the numerous tests performed by the testers to validate the quality of the product, these play a crucial role in the software development lifecycle. Hence, by implementing and executing these software testing metrics and performance indicators you can increase the effectiveness as well as the accuracy of your testing efforts and get exceptional quality. Thanks for such a great article here. It was definitely interesting for me to read about their market situation nowadays.

Well written article Thank You for Sharing with us. Thanks for the above article. It is very informative. I am planning to introduce some in to my current project also. And a great thanks for sharing the same.

This is my 1st time Ive visited this site. I identified a whole lot of interesting info within your blog. From the volume of comments on your posts, I guess I am not the only 1! The more effort your team invests in the quality control and assurance, the less problems you will see reported daily.

Instead of fixing obvious the problem, now you need to read between the lines and understand what issues less obvious ones prevent users from unlocking the true potential of your product. Before, we discussed how to measure complexity , and here are some key points to look at:. Here is an example of how eliminating unnecessary complexities can help organizations to improve. Sometimes, quality specialists have to justify their budgets and strategies in board meetings.

Here are some finance-related quality metrics to use. Use this metric to understand the impact of poor quality. Estimate all costs associated with problem detecting and solving:. This indicator summarizes the values of the indicators inside, giving us the total cost of quality. In my example, the calculations are for the average cost per accident. Its formula depends on the specific situation.

For this example, we can calculate the budget needed to decrease the defects rate from 0. The resources of any organization are limited. We cannot treat all quality issues equally; some need to be solved immediately, and some will stay in your backlog for a longer time.

How to justify decision making in this case? A simple opportunity or risk estimation will help:. A few years ago, we got two feature requests from the customers both functions are implemented now :.

A was a critical issue for a big potential client, and three other clients had previously asked about this. B was a less critical issue for the existing client and only one more client had requested a similar feature. For now, we were quantifying and measuring the impact of poor quality. To predict some important quality problems, we need to look at the preceding, less important problems. You need to teach your team to report and discuss minor quality issues that might lead to more important problems.

Similar to the Near miss metric , but in this case, we want to be proactive. The requirements, in this case, are the preceding documentation, so if we make the requirements better, we can prevent future quality problems proactively.

Intuitively, software developers understand that:. In other words, we expect code quality to be the function of code age and the number of code modifications. Thinking about this in the context of quality strategy, we can predict defects more effectively and update testing strategy, respectively.

Similar to the requirement ambiguity metric , but in this case, we can ask experts to review anything — our product, user manual, customer service. How to quantify such reviews? A humoristic still practical response to this question was given by Thom Holwerda in his comics for osnews :.

The original metric was for software code quality reviews, but as you can imagine, it works in any domain. Obviously, this approach works on early product stages where there are often quality issues.

With the development of quality control systems, the size of the aleatory sample should be increased. The idea of self-test is to introduce the input with known errors and see if those pre-engineered problems were detected.

Medical analysis laboratories have this routine implemented. The laboratory equipment is a combination of complex optic devices and chemical reagents. The factors of quality are the way reagents are stored, the quality of distilled water, the stability of the electric current, human factors, and random errors off course. The easiest quality control, in this case, is to load a known set of samples and compare actual results with expected ones.

Regulators use the same approach doing blind tests when the properties of the test samples are not known beforehand. Similar to the self-test, but in this case, we are not looking at the system as a black box, we can do more specific automated tests.

Still, we need to balance automated test coverage with the cost to develop and maintain such tests. The idea is to foresee the possible conditions and try to be prepared. Imagine a best- and worst-case scenario and try to come up with emergency plans.

For example, to illustrate this article and create a scorecard for quality, I was using our BSC Designer software. This always helps in generating some improvement ideas.

When the complexity of the product is high, changes in the product might produce many quality issues. The solution to this challenge lies in the level of product architecture. For example, software engineers build their solutions using microservices to avoid bad dependencies.

From the viewpoint of quality, the starting point is to estimate risks of change. Ask your engineers:. We tried to fix the problem on the design stage. We tried to fix the problem before the customer or other stakeholders saw it. But sometimes, a problem passes all these filters, and customers face a quality issue. We discuss below the metrics that we can track in this case.

In many cases, the problem is not that critical for the client, and what is more important is that your team care. Similar to the sales backlog , a quality backlog is an indicator of how quality flows throughout your organization. Overloaded backlog shows that your response efforts are not sufficient, and something needs to be changed. Imagine that a company did a quality audit. For example, using the requirements ambiguity analysis described above. We need to make sure that those findings are analyzed and closed.

We are not always talking about implementing the recommendations, but at least they need to be reworked into some tangible form for example, updated requirements. The complexity is a known leading factor of quality.

Did the customer need to make just one call to fix the problem, or it was an exchange of five emails to solve a simple issue? We fixed the problem, and we communicated efficiently and effectively along the process, but was the problem actually solved? These metrics work best if bundled with the time to solve metric. The organization needs to find a balance between the speed of providing solutions and their effectiveness.

Quality culture is about establishing new behaviour habits that will help to avoid critical problems and maximize the effectiveness of solving less critical issues. Imagine a scenario when a user reports a problem in your product, but it appears not to be a problem but normal behaviour. As long as quality is perceived by the clients, this situation will be considered by a customer as a defect.

What does a rejected defect mean? It means that some other system did not work well. Metric to track:. A similar situation is when a tester of the product reports a problem that is rejected by the engineers. Where is the problem in this case? I believe it is in the ambiguity of the initial requirements. Fixing quality issue on time is a good starting point, but an organization also needs to learn from its mistakes see the quality assurance article on Wikipedia.

Do a root cause analysis for quality issues:. Off course, the degree of root-cause analysis should correspond to the importance of the product. Part of the quality assurance challenge is to detect and find systematic errors. A release cycle comprises different stages from development and testing to deployment and tracking. Long release cycles can be detrimental in very competitive markets.

Thus, organizations often look to speed up their release cycles. However, a focus on speed could lead to a decrease in the quality of the products. Nevertheless, by implementing best practices in software release management, you can shorten your release cycles without sacrificing quality. Here are some ways to do that. Documenting release plans is a great way to ensure that everyone is on the same page.

A release plan should contain your goals, quality expectations, and the roles of participants. After documenting your release plans, ensure that all team members can access, reference, and easily update them as needed. A copy will also be sent to your email address. Automating manual and repetitive tasks can be a great way to speed up your release cycle while maintaining quality. QA automation frees up valuable human resources, which you can then reallocate to work on other high-priority tasks.

Some possibilities include automated regression testing, code quality checks, and security checks. After assessing the state of your release process, create a regular release schedule.

Doing this will help create a routine system that your teams can get comfortable with. End users will also know when to expect updates and are more likely to engage with the latest releases. Having a target release plan will help your teams work towards the release dates while achieving current goals in the release cycle. Hidden bottlenecks in your release infrastructure could slow down the deployment process. Thus, you must optimize your delivery infrastructure and implement practices such as continuous testing and testing automation.

A release retrospective involves reviewing the processes in past releases to extract insights that can help you improve those processes in future releases. Release retrospectives provide teams with an open environment to analyze past problems and create strategies to avoid them in the future.

However, to ensure that your release cycles are consistent and that they run smoothly, you may need to evaluate the effectiveness of the QA in your software development. Analyzing your current system by using metrics can help you understand which areas need improvement. Without the use of metrics for quality assurance, it would be challenging to measure software quality. Software test metrics are standards of measurement that QA teams use to assess quality in software development projects.

Quality metrics in software testing enable just that—improved QA processes. In return, optimizing QA processes helps budget for testing needs more efficiently. As a result, they can make informed decisions for future projects, understand what needs to be improved, and make the necessary changes. So, what kinds of metrics can help you make these decisions? There are a lot of QA metrics that are more or less valuable for your current scenario, that is, the current state of your project.

A metrics value can be defined by how actionable it is—the measurement can result in improvement—and if it can be constantly updated. Here are a few metrics examples that can be relevant to your project in its current state:. What quality KPIs do you use in your software development projects? How you interpret them? Thank you all in advance for help! All processes are very loosely based on CMMI. The project is similar to SAP. We have a modules which are the core of the system, lower levels are common for smaller parts of stakeholders or customize.

We are working in a mode of support of current released system versions fixing bugs, adding users etc. The project is web application.

For example, we want 30, new customers in this year. We check our actual lead time and see check whether this time is enough to stay on the market. For example, we want to reduce our current day long lead time to 9 days in this year, because the competition became faster then us. Additionally, the set numbers are not achievable with our current way of working, so the organization has to work and improve in order to achieve them. We have automated tools to get the data above and we regularly check on our indicators whether we are on the right track or not.

If not, we change our way of working, try out new stuff etc. I give you an example. In my friend's organization one of the main KPIs is the number of defects in the system. As far as I remember, the goal is to have maximum 10 defects. They keep this number, even if it is not necessary.

Their customer can live with 20 or 30 defects, he wants to have a system with fast reaction time, but the developers spend time on defect fixing and not on making the system more faster.

There is chance that they'll lose this customer over this. There are several KPIs -what we call a quality model- you can use to measure a software product quality.