Defining and Tracking KPIs for QA Success

Reliable and high-performing software applications can only be pushed out with good quality assurance measures. We at Kualitatem pride ourselves on providing top-of-the-line QA services.

So, trust us when we say that software quality assurance is more than just hunting for bugs. It’s also about measuring, optimizing, and delivering value, which is done by strategically crafted Key Performance Indicators (KPIs). These KPIs play a big role in aligning QA efforts with business goals.

That being said, this article will explore how you can define meaningful KPIs for QA processes. We’ll also talk about the best practices for tracking them and how these metrics can boost your QA success.

Why QA Needs KPIs?

KPIs are basically metrics that help you measure the effectiveness of QA processes over time. They also make it easy to determine the efficiency and impact.

Think of KPIs as a compass. One that guides QA teams towards achieving the set goals and uncovering bottlenecks. With KPIs, you can ensure that standards are consistently met and even exceeded in some areas.

Organizations that make use of clear KPIs usually experience significant gains. According to the Journal of Applied Psychology, almost a 20% increase in overall work productivity can be seen. On top of that, a 15% boost in customer retention can also be observed.

Defining Effective QA KPIs

Not every metric is supposed to be equal. The most effective KPIs are those that achieve the following things.

• Align completely with the business and project objectives
• Are measurable and actionable, all while being relevant
• Give insight into the process and software quality
• Bring continuous improvement and accountability

Steps to Develop QA KPIs

There are five main steps to come up with good KPIs.

1.     Establish Clear Objectives

Start by thinking about what success looks like for your QA team. Is it reducing defects in software applications? Or is it improving customer satisfaction? Or maybe it’s to accelerate release cycles?

The objectives and what you expect from your team should be clear. They have to be specific and tied to business outcomes.

2.     Involve Everyone Who’s Relevant

After the objectives are set, you must call everyone who is involved in the QA cycle. Do this to gather their opinions.

Engage with QA testers, developers, and project managers to ask them for their perspective. What do they think quality means for your service or software product?

3.     Define Quantitative Metrics

Next up, choose metrics that are easy to measure. They shouldn’t be vague or confusing. A couple of examples include defect density and test overage. Customer complaints and average resolution time are also things that can be considered.

4.     Set Benchmarks & Targets

KPIs have to have realistic targets. To achieve this goal, take a look at the industry standards, historical data, and conduct competitive benchmarks.

Doing so will provide you with context and help teams understand what exactly “good” looks like.

5.     Document & Communicate

Come up with clear definitions for each KPI. Store them in a glossary or reference table. Then, provide this table to the team. Doing this will help ensure that everyone involved understands what each metric measures and why it matters.

Important QA KPIs to Track

We’re listing some KPIs below that make the most impact for QA teams. Their significance and the best practices to track each KPI are also mentioned.

1.     Test Coverage

Test coverage keeps track of the percentage of requirements, code, and features that are covered by test cases. Considering this, high test coverage reduces the risk of undetected defects. It enables a comprehensive review of the software.

How to Track:

It’s best to use requirement coverage metrics along with automated tools. Map the test cases to requirements and code modules.

Best Practice:

We recommend aiming for 100% requirement coverage. However, do balance things with risk-based testing to prioritize critical areas.

2.     Defect Density and Error Rates

The number of defects that are found in a specified unit is called defect density. The unit can be 1000 lines of code, maybe less, maybe more, whatever you think would be right.

On the other hand, error rates track the frequency and types of errors that occur during testing. The errors that come up in production can also be counted.

How to Track:

Use a defect tracking system, such as Kualitee. Log, categorize, and analyze defects using the platform.

Best Practice:

Take the industry averages and benchmark against them. Set targets to reduce defect density and error rates in the long run.

3.     Escaped Defects

These are the issues that somehow reach the end-users. The ones that are missed during quality assurance. This KPI reflects the real-world effectiveness of your testing processes.

How to Track:

Monitor the customer feedback. They usually report defects and other issues from production.

Best Practice:

Carefully analyze the root causes and adapt your testing strategies. This will help catch similar issues earlier.

4.     Percentage of Rejected Defects

This metric is all about the defects that are reported by testers but rejected by developers. Most of the time, this happens because of misclassification or due to the fact that developers can’t produce the code differently.

How to Track:

Once again, defect management tools come in handy here. They record the status of every reported defect and show it to the relevant authorities.

Best Practice:

If the rejection rates are high, you should provide more training to the testers. Before doing so, though, take a look at your documentation and ensure that it is clear. If not, start with making it clearer.

5.     Time to Test/Average Resolution Time

How long does it take to move a feature from “in testing” to “complete”? This is what is measured using this KPI. The average time to resolve the defects that are reported is analyzed.

How to Track:

Workflow management tools that allow timestamping each stage of the testing lifecycle should be used.

Best Practice:

We know that efficient processes are indicated by shorter times. But it is important to balance speed with thoroughness for resolving defects.

6.     Customer Satisfaction Scores

The ultimate goal of QA is to deliver software applications that delight customers. So, you have to find out if they are happy with your work or not.

A few ways to do that are by taking surveys, giving feedback forms, or through Net Promoter Scores (NPS).

How to Track:

You have to regularly ask for feedback from users. Especially when a new release or update is pushed.

Best Practice:

Keep a lookout for trends overtime. Then, correlate them with your QA processes. This will help push new things that are in demand with your software.

7.     Operational Efficiency Metrics

KPI that include data on resource utilization are known as operational efficiency metrics.

Apart from resource utilization, you can also add the cost per defect and the percentage of automated test cases to it.

How to Track:

The easiest way to do this is by using the analytics dashboards provided by QA management tools.

Best Practice:

Keep an eye out for opportunities to automate repetitive tasks. It will reduce human labour and optimize resource allocation.

8.     Audit Findings and Compliance

Perform audits from time to time. They ensure adherence to standards and enable identifying risks early. If there are just a few negative findings, it means the QA processes are up to the mark.

How to Track:

Document the results of every audit and track remediation efforts. Doing so will enable you to revisit stuff later if needed.

Best Practice:

It’s best to prepare for auditing. It shouldn’t be a last-minute scramble. Make it a continuous process so your team is always ready.

9.     AI-Driven KPIs

Since AI has pretty much become an integral part of QA, new KPIs have to be made. A couple of them include predictive accuracy and model performance. There are many others as well, but these two are a good starting point.

How to Track:

Leverage AI analytics platforms to monitor model drift. Make sure that there are no prediction errors and everything follows compliance rules. All this can be done from inside the analytics platform.

Best Practice:

Continuously re-train and validate AI models so they don’t provide outdated predictions. High performance can only be achieved through constant updates.

Implementing and Monitoring QA KPIs

It’s important to monitor how your KPIs are performing. They should have made positive changes to the workflow. Here’s how you can go about this:

• Cycle-Wise Monitoring:

You can break down your projects into short cycles. i.e., two weeks. Then, review KPI performance at the end of each cycle. This will help in the timely identification of issues and quick course correction.

• Quarterly Review:

Quarterly reviews include taking data from multiple testing cycles for a broader view. They help in spotting long-term trends. It’ll be easier for you to recognize areas of excellence and share the best practices across teams.

• Continuous Improvement:

QA KPIs should never be static. So regularly refine metrics based on feedback. Besides feedback, you can also consider the changing business goals and industry shifts to refine KPIs. Another thing that can be done is to encourage your team members to suggest improvements themselves.

Example QA KPI Table

This table will help you better understand the important KPIs that we talked about before.

KPI	Current Value	Target Value	Industry Average
Test Coverage	85%	95%	90%
Defect Density	5/1,000 LOC	4/1,000 LOC	0.65/1,000 LOC
Escaped Defects	12/month	5/month	7/month
Customer Satisfaction Score	88%	92%	85%
Avg. Resolution Time	4 days	2 days	2 days

Final Words

To measure the effectiveness of the QA process over time, establishing KPIs is important. And for that, you have to come up with clear objectives, involve relevant people, define quantitative metrics, set targets, and document everything.

Furthermore, some important QA KPIs that should be tracked are test coverage, defect density, escaped defects, and many others that we’ve mentioned in this post.