Best Practices and Metrics for Successful Production Testing
The software deployment process typically progresses through stages: development, quality assurance, staging, and production. While staging environments offer controlled testing, they can be time-consuming to set up and might miss certain end-user scenarios. Consequently, many developers prefer direct production testing to address issues in real time. Yet, it's crucial to measure the effectiveness of this approach to prevent potential setbacks in user experience.
Best Practices for Production Testing
Production testing, as the name suggests, is the process of testing software in a production environment. It serves various benefits. For example, developers can ensure that:
- Testing is highly accurate, and all the scenarios not imitated in the staging environment are covered.
- They can identify and fix bugs not found in pre-production testing.
- The software is compatible with the production environment.
- They can effectively drive testing initiatives like A/B testing, which can only be carried out in production.
- They can gather user feedback and incorporate it in real time.
- There’s less damage to the application in the event of errors cropping up during production.
All these capabilities serve to ensure that the software works as expected and does not cause any problems for users. To that end, let's understand the best practices in testing software in production.
Create a Deployment Strategy Before Starting with Production Testing
It's important to establish a deployment strategy before initiating production testing. How? Well, a deployment strategy can help ensure that testing is being carried out in a controlled and orderly manner. As such, a deployment strategy constitutes:
- The types of tests to be performed
- The order in which the tests will be performed
- The resources needed for testing
- The communication plan for communicating the results of testing to stakeholders
A concrete deployment strategy translates into greater testing accuracy and reduced risk of issues transpiring. It also paves the way for automating the deployment process altogether.
Establish Realistic Test Schedules
The test schedules should be realistic and achievable. They should also be flexible enough to allow for changes as needed. So, when setting up the test schedules, it is important to consider the following factors:
- The complexity of the software
- The available resources
- The objectives of testing
- The timeline for releasing the software to production
That said, it pays to follow these tips to ensure realistic test schedules for production testing:
- Break down the testing into smaller, more manageable tasks.
- Estimate the time it will take to complete each task.
- Allow for some buffer time in case of unexpected problems.
- Communicate the test schedules to all stakeholders and get their buy-in.
Automate Production Testing Wherever Possible
A host of testing types, like performance, integration, security, and acceptance testing, fall in the purview of production testing. Automating these not only helps enhance the overall testing efficiency but also frees up human testers to focus on exploratory testing. Automated testing can effectively perform repetitive tasks, such as running unit tests or integration tests.
Follow these tips to ensure success with the automation of production testing:
- Leverage a test automation tool that befits your production testing requirements.
- Create well-structured test scripts that are easy to maintain and update.
- Use a test data management system to store and manage test data.
- Run automated tests regularly to ensure that the software works as expected.
Track & Analyze Results
One of the production testing best practices is to keep track of the outcomes. After all, that'll help ensure whether the new features or code comply with the pre-set usability requirements.
For effective production testing, it's essential to:
- Monitor various testing methods for effectiveness
- Analyze test outcomes to pinpoint root causes
- Implement necessary corrective actions
- Compile comprehensive reports summarizing test results
But how should developers go about tracking in the first place? This is where production testing metrics become viable.
How Can Production Testing Metrics Play a Role?
Production testing metrics are invaluable tools in the software development process. They provide quantitative data and insights into the quality, efficiency, and performance of testing in production.
What Are the Types of Production Testing Metrics?
In essence, production testing metrics can be grouped under three major categories, i.e., process, product, and product metrics. But, for ease of understanding, we'll look at some of the most pertinent metrics that matter in the purview of the current discourse.
Metrics Specific to Testing Efficacy
From the percentage of test coverage to the number of defects identified, a host of testing metrics contribute to the narrative around testing efficacy. For example, metrics like function coverage, statement coverage, test pass rate, defect density, test creation time, test execution time, reopened defects, age of defects, environment downtime, etc., effectively help define the production testing efficacy.
Performance Metrics
Given how quickly users can churn, monitoring the performance facets is exceptionally critical. It's here that the measurement of latency, throughput, resource usage, error rate, etc., becomes viable.
User Experience Metrics
Since the end goal is to ensure that users can seamlessly use the app for their requirements, monitoring user experience metrics like load time, session duration, bounce rate, depth of interaction, etc., becomes crucial. Developers can even keep tabs on the customer effort score (CET) and customer satisfaction score (CSAT) for incorporating pertinent feedback.
In addition to the above, developers can monitor security facets by understanding the number of security incidents, the time taken to patch vulnerabilities, etc.
Manual vs. Automated Testing Metrics
Both manual and automated testing metrics serve specific roles in the production environment:
Manual Testing Metrics
Often, when performing manual testing in production, the focus is on real-world scenarios and usability. In that light, usability metrics, user feedback, subjective performance metrics (like perceived responsiveness), accessibility metrics, etc., become viable.
Manual testing metrics also cover absolute metrics like total test cases, failed test cases, or deferred defects. Then there are derivative metrics like fixed defects percentage, average time to fix defects, etc.
Automated Testing Metrics
In automated testing, the focus is more on through and through monitoring and validation of the app's functionalities. So, metrics associated with test execution (execution time, fail rate, skipped tests), coverage, reliability (flaky tests, stability score), etc., gain importance.
However, it's noteworthy that the absolute and derivative metrics remain the same if automation replicates manual testing efforts to reduce the cost and time of testing.
Conclusion
Indeed, testing in production has its own benefits. However, ensuring success with production testing entails following the aforementioned best practices and keeping tabs on a multitude of pertinent metrics.
What also matters, especially for automated testing efforts, is that developers must have access to a robust test automation platform like ACCELQ that can quicken the validation process and provide them with a holistic view of testing outcomes. A codeless platform like ACCELQ can bring the necessary discipline, flexibility, and scalability to production testing. Get in touch to learn more.
Yuvarani Elankumaran
Technical Consultant at ACCELQ
Yuvarani Elankumaran is a highly skilled technical consultant at ACCELQ. With over a decade of experience in the field of Test Automation, Yuvarani is a seasoned professional who is well-versed in a variety of programming languages and automation frameworks.
