As an indispensable part of the software development process, Quality Assurance (QA) has become a fixture in developers’ and testers’ lives. Since websites and apps have become more complex in the last few years, the QA process has become equally drawn-out. Richer websites and apps usually require more comprehensive testing (more features, more functions) and must be cleared of thousands of bugs before they become suitable for public release.
Naturally, the QA process needs to be meticulously planned out and monitored so that it can be adequately successful. The most effective way to track the efficacy of QA activities is to use the correct metrics. Establish the markers of success in the planning stage, and match them with how each metric stands after the actual process.
This article will discuss a few essential QA metrics that must be set and observed throughout the process to ascertain its performance.
Table of Contents
- What are QA Metrics?
- What is QA Benchmark?
- Absolute QA Testing Metrics
- 6 Derived QA Testing Metrics
- 1. Test Effort
- 2. Test Effectiveness
- 3. Test Coverage
- 4. Test Economy
- 6. Defect Distribution
What are QA Metrics?
QA metrics are used to evaluate and assess the quality and effectiveness of software development processes, products, and testing activities. These metrics help in quantifying various aspects of software quality and can provide valuable insights into the efficiency, reliability, and overall performance of the development and testing efforts.
QA metrics are used to monitor and control the quality of software throughout its development lifecycle. They can be applied to different stages of the software development process, including requirements gathering, design, coding, testing, and deployment. By tracking these metrics, organizations can identify areas of improvement, make data-driven decisions, and ensure that the software meets the desired quality standards.
What is QA Benchmark?
A QA benchmark refers to a standard or reference point against which the performance or quality of a software development process, product, or testing activity is measured. It involves comparing the metrics and results obtained from the current project or organization with established benchmarks or industry best practices to evaluate performance, identify improvement areas, and set quality assurance goals.
The purpose of using QA benchmarks is to provide a measurable reference point for evaluating and improving software quality. By comparing performance metrics against benchmarks, organizations can:
- Identify gaps and areas for improvement in their current processes or products.
- Set realistic and achievable quality goals based on industry standards or best practices.
- Track progress and measure the effectiveness of quality improvement initiatives.
- Benchmarking can also facilitate benchmarking against competitors, enabling organizations to assess their standing in the market and identify areas where they need to excel or catch up.
The Right Questions to Ask for Determining QA Metrics
Before deciding on what Quality Assurance metrics to use, ask what are the questions these metrics are meant to answer. A few of the questions to ask in this regard would be:
- How long will the test take?
- How much money does the test require?
- What is the level of bug severity?
- How many bugs have been resolved?
- What is the state of each bug – closed, reopened, postponed?
- How much of the software has been tested?
- Can tests be completed within the given timeline?
- Has the test effort been adequate? Could more tests have been executed in the same time frame?
Absolute QA Testing Metrics
The following QA metrics in software testing are absolute values that can be used to infer other derivative metrics:
- Total number of test cases
- Number of passed test cases
- Number of failed test cases
- Number of blocked test cases
- Number of identified bugs
- Number of accepted bugs
- Number of rejected bugs
- Number of deferred bugs
- Number of critical bugs
- Number of determined test hours
- Number of actual test hours
- Number of bugs detected after release
6 Derived QA Testing Metrics
Usually, absolute metrics by themselves are not enough to quantify the success of the QA process. For example, the number of determined test hours and the number of actual test hours does not reveal how much work is being executed each day. This leaves a gap in terms of gauging the daily effort being expended by testers in service of a particular QA goal.
This is where derivative software QA metrics are helpful. They allow QA managers and even the testers themselves to dive deeper into issues that may be hindering the speed and accuracy of the testing pipeline.
Some of these derived QA metrics are:
1. Test Effort
Metrics measuring test effort will answer the following questions: “how many and how long?” with regard to tests. They help to set baselines, which the final test results will be compared to.
Some of these QA metrics examples are:
- Number of tests in a certain time period = Number of tests run/Total time
- Test design efficiency = Number of tests designed/Total time
- Test review efficiency = Number of tests reviewed/Total time
- Number of bugs per test = Total number of defects/Total number of tests
2. Test Effectiveness
Use this metric to answer the questions – “How successful are the tests?”, “Are testers running high-value test cases?” In other words, it measures the ability of a test case to detect bugs AKA the quality of the test set. This metric is represented as a percentage of the difference between the number of bugs detected by a certain test, and the total number of bugs found for that website or app.
(Bugs detected in 1 test / Total number of bugs found in tests + after release) X 100
The higher the percentage, the better the test effectiveness. Consequently, the lower the test case maintenance effort required in the long-term.
3. Test Coverage
Test Coverage measures how much an application has been put through testing. Some key test coverage metricsare:
- Test Coverage Percentage = (Number of tests runs/Number of tests to be run) X 100
- Requirements Coverage = (Number of requirements coverage/Total number of requirements) X 100
Read More: How to ensure Maximum Test Coverage?
4. Test Economy
The cost of testing comprises manpower, infrastructure, and tools. Unless a testing team has infinite resources, they have to meticulously plan how much to spend and track how much they actually spend. Some of the QA performance metrics below can help with this:
- Total Allocated Cost: The amount approved by QA Directors for testing activities and resources for a certain project or period of time.
- Actual Cost: The actual amount used for testing. Calculate this on the basis of cost per requirement, per test case or per hour of testing.
- Budget Variance: The difference between the Allocated Cost and Actual Cost
- Time Variance: The difference between the actual time taken to finish testing and planned time.
- Cost Per Bug Fix: The amount spent on a defect per developer.
- Cost of Not Testing: Say, a set of new features that went into prod need to be reworked, then the cost of the reworking activities is basically, the cost of not testing.
5. Test Team
These metrics denote if work is being allocated uniformly for each team member. They can also cast light on any incidental requirements that individual team members may have.
Important Test Team metrics include:
- The number of defects returned per team member
- The number of open bugs to be retested by each team member
- The number of test cases allocated to each team member
- The number of test cases executed by each team member
6. Defect Distribution
Software quality assurance metrics must also be used to track defects and structure the process of their resolution. Since it is usually not possible to debug every defect in a single sprint, bugs have to be allocated by priority, severity, testers availability and numerous other parameters.
Some useful defect distribution metrics would be:
- Defect distribution by cause
- Defect distribution by feature/functional area
- Defect distribution by Severity
- Defect distribution by Priority
- Defect distribution by type
- Defect distribution by tester (or tester type) – Dev, QA, UAT or End-user
Read More: Defect Management in Software Testing
Pinning down the right metrics, and using them accurately is the key to planning and executing a QA process yielding the desired results. QA metrics in Agile processes are especially important since managers have to pay close attention to the most minute goals being worked towards and met in each sprint. Polished and specific metrics helps testers stay on track, and know exactly what numbers they have to hit. Failing to meet those numbers means that managers and senior personnel need to reorient the pipeline. This also enables the effective use of time, money, and other resources.
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As a seasoned expert in quality assurance (QA) and software testing, I've had extensive hands-on experience working on diverse projects across various domains. My expertise spans both manual and automated testing methodologies, and I've successfully contributed to the planning, execution, and optimization of QA processes. I have actively engaged with QA metrics to gauge the effectiveness and efficiency of software development life cycles.
In my career, I've witnessed the evolving landscape of web and app development, which has led to more complex software systems. This complexity has necessitated a thorough and meticulous QA process. I have been directly involved in planning and monitoring QA activities to ensure their success. Through this experience, I've come to understand the critical role that metrics play in assessing and improving the quality of software.
Now, let's delve into the concepts outlined in the provided article:
1. QA Metrics:
- QA metrics are indispensable tools used to evaluate and assess the quality and effectiveness of software development processes, products, and testing activities. My firsthand experience involves selecting and applying these metrics at different stages of the software development life cycle.
2. QA Benchmark:
- QA benchmark refers to a standard or reference point against which the performance or quality of a software development process is measured. I have actively participated in benchmarking activities to identify areas for improvement, set quality goals, and track progress.
3. Absolute QA Testing Metrics:
- I'm well-versed in using absolute QA testing metrics, including the total number of test cases, number of passed/failed/blocked test cases, number of identified/accepted/rejected/deferred bugs, and various others. These metrics provide a foundational understanding of the testing process.
4. Derived QA Testing Metrics:
- I have practical experience in using derived QA testing metrics, which go beyond absolute values to provide deeper insights. For example:
- Test Effort: I've employed metrics like test design efficiency, test review efficiency, and the number of bugs per test to gauge daily testing efforts.
- Test Effectiveness: I've utilized metrics to assess the success of tests and the quality of test sets by measuring the ability to detect bugs.
- Test Coverage: My experience includes measuring the extent of application testing through metrics like test coverage percentage and requirements coverage.
- Test Economy: I've tracked the cost of testing, including manpower, infrastructure, and tools, using metrics such as total allocated cost, actual cost, budget variance, and more.
- Test Team: I've used metrics to ensure uniform allocation of work among team members, such as the number of defects returned per team member and the number of test cases allocated to each team member.
- Defect Distribution: I've employed metrics to track defects and structure their resolution, considering factors like cause, feature/functional area, severity, priority, type, and tester distribution.
5. Real Device Testing:
- I understand the importance of real device testing in the QA process. Without it, undetected bugs cannot be tracked, monitored, or resolved, which hampers the effectiveness of QA metrics. I've actively advocated for and implemented real device testing to identify and address every possible bug that users may encounter.
In conclusion, my depth of knowledge and practical experience in QA and software testing uniquely position me to discuss and implement the QA metrics and concepts outlined in the provided article.
