Can you release an application to customers without verifying that it actually works? No? This is because, before software deployment, it is very important to ensure that the app not only meets the prescribed standards but also stands up to the end user’s requirements. Due to the rising complexity of modern apps, you need to use AI QA testing processes to achieve these goals.
In this process, you have to use machine learning, statistical methods, and practical analytics to do much more than simply finding out defects in the application. You can anticipate the errors, measure their potential impact, and prioritize them according to their business value.
Still confused about intelligent reporting and analysis in QA processes? Don’t worry! We are here for you. Our article will help you understand how to implement intelligent reporting and analysis in QA processes, some of the most common strategies related to it, along with the best practices for the overall implementation process.
Why Reporting In QA
Before we start with the implementation process, let us take a step back and understand that why should we implement reporting in QA processes and implement all the required resources for it. To shed more light on this answer, let us go through some of the major challenges that it aims to solve:
- With reporting, you can access detailed reports of any defects that have been discovered in the application infrastructure. These reports will highlight the nature of the defect, how to reproduce it, its severity, and all the components or modules that have been affected by it.
- Reporting will give access to test coverage metrics which will show what percentage of the code base or requirements or features have been currently tested with the workflow.
- This process will also give access to various comprehensive test reports that will show the outcome of each test run. This will categorize the test cases into failed, passed, or skipped segments.
- You will also have access to detailed performance metrics, which will show how the system behaves under load, including its response time and the overall resource usage during the execution process.
- The reporting process will finally also include various trends and patterns, including recurring defects in certain modules or performance slowdowns at specific stress levels within the application infrastructure.
Evolution of Intelligent QA Reporting
Since its initial inclusion, intelligent keyword reporting has come a long way to incorporate various advanced parameters. Under the hood, the QA reporting will basically revolve around the automation of data captures and analysis along with the application of advanced analytics to include various meaningful insights.
Automated Data Capture
Intelligent QA reporting has completely eliminated those days when you have to manually log every test result and defect within a specific spreadsheet. With the integration of continuous deployment and continuous integration pipelines, the built pipelines will automatically trigger the test suite with every code commit and capture the following information:
- Test logs, including the console outputs, screenshots, stack traces on failures, and every important information that has been recorded during the execution process.
- Details of the code coverage, like real-time coverage metrics and instrumental build data.
- Various important performance benchmarks like CPU, memory, and response time metrics that will be useful while executing and reviewing performance testing scenarios.
- Finally, it will also have integration with tools like Azure DevOps or Jira to automatically create or update defects with data from test failures. This approach will ensure that you will require minimal human involvement to resolve the errors.
Advanced Analytics
Intelligent reporting isn’t just about storing large amounts of data. It’s also about predicting, prioritizing, and preventing errors in the future releases of the application. So, while implementing advanced analytics in QA, you will need to incorporate the following parameters:
- Trend analysis to analyze the historical data for defect density, test coverage, and build pass rates. This will help you to identify if a certain module repeatedly fails or if the total test coverage in a critical area is not sufficient.
- You have to also include predictive modeling using machine learning algorithms on historical defect data to predict which area of the code is more prone to future defects. It will be very important information for planning, risk assessments, and resource allocation in the future.
- Root cause analysis will use clustering or anomaly detection to identify systematic issues in the codebase or the testing process. You can consider the example of a memory leak immediately occurring in a module, which will depend on a specific external library. In such a case, the system will flag the dependency as high risk.
- Finally, you can use sentiment analysis in scenarios where QA extends through production monitoring. This will help you analyze real user feedback or support tickets to reveal potential issues before they manifest on a larger scale.
Dashboards and Visualization
While implementing intelligent reporting and analysis, you have to pay special attention to data visualization. In such a case, interactive dashboards will allow all the team members, like developers, testers, managers, and executives, to understand complex datasets at one glance. For this purpose, the system will use graphs, heat maps, and color-coded charts to highlight areas that demand immediate attention.
Using all this visual information, you can bridge the technical gap and align the stakeholders quickly to focus on the most important areas of the project.
Continuous Feedback Loop
Finally, by adding QA instrumentation in every part of the development lifecycle, you can create a continuous feedback loop. This means that every commit, build, and production deployment will create real-time data. You can also use automated dashboard updates and predictive model adaptation to find errors or anomalies in real-time. This is one of the most important values of modern QA processes, proactivity.
Tools and Technologies for Intelligent QA
The world of QA is very massive as it focuses on multiple areas like test management, defect tracking, automation, analytics, and integrated QA platforms. To simplify this segment, we have mentioned some of the most important tools and technologies that will help you to create a holistic ecosystem of your own:
- Test Automation Frameworks
To automate the QA testing processes, you will require access to various test automation frameworks. To implement this, you can consider options like Selenium WebDriver, Cypress, or JUnit. All of these frameworks are equally competent and serve different requirements.
So, we suggest the testers to thoroughly understand their requirements, expertise, and their investment capabilities to choose the perfect option based on all these factors.
- Cloud Testing Platforms
While implementing QA testing with AI test tools, you will also require access to multiple real devices to understand the performance fluctuations in the application when subjected to the physical parameters of a device.
Considering that it is almost a nightmare to set up and maintain a real device lab, we suggest the use of cloud-based device labs that can be accessed through platforms like LambdTest. LambdaTest is an AI-powered test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ real devices, browsers, and OS combinations.
- CI/CD Pipelines
To integrate continuous integration and continuous deployment pipelines within the application workflow, you can consider using tools like Jenkins, GitLab CI, or Azure DevOps pipelines. This will allow you to integrate rich test logging along with plugin support for multiple QA analytics processes.
- Defect Tracking And Management
To properly keep track of all the defects and errors that have been found in the application infrastructure, you can use test management tools like Jira software or Azure DevOps board. These tools will integrate smoothly with the development and QA tools to ensure that you are streamlining the entire ticketing and analytics process.
- Data Visualization And BI Tools
Tableau is a tool that helps in data visualization to allow QML managers to build interactive dashboards which can combine data with multiple QA and DevOps systems. On the other hand, Power BI is Microsoft’s native business intelligence platform that you can add with Azure DevOps and other Microsoft ecosystem tools.
Best Practices in Intelligent QA Reporting
Finally, let us go through some of the best practices that we recommend the testers to incorporate within their QA reporting processes. These practices will help you improve the smoothness, sustainability, and quality of the entire workflow:
- It is very important to automate the entire testing pipeline, from triggering tests to extracting logs and creating defect tickets, to ensure that you have real-time reporting data.
- We suggest the testers to integrate QA at the earliest stages of the development cycle to utilize static code analysis and unit tests for immediate feedback.
- You must remember that not all the tests will be equally important to ensure the proper functioning of the application. So, you must prioritize the tests according to their function in the core stability of the app cycle and conduct them first.
- Quality assurance isn’t a one-time job. So, you must track the performance metrics, error logs, and user feedback even after the deployment of the application to create a complete product quality insight.
- While creating the QA reports, you must ensure that they don’t just display data but also provide content, trends, and recommendations so that the stakeholders can act upon them.
- We suggest the testers to create the reporting practice in such a way that it is crafted for different audiences. For example, the developers will need technical defect logs, while the executives will require high-level business-focused metrics to understand the impact of the application.
- Finally, you must invest in QA team training. This is because the tools and analytics will be as good as the people who are using them. You can consider using ongoing education courses, analytics approaches, and market-based practices for the training purposes.
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that intelligent reporting and analysis is much more than just a market craze. It is a very important evolution to keep up with modern software and product development challenges. By adding all the strategies and best practices that we mentioned in this article, you can create a proactive and predictive quality culture.
You should also remember that apart from the advanced technological advancements, you also have to plan the human implementations like careful planning, iterative implementation, and cross-functional collaboration. This is because the proper balance between humans and machines will be the most important parameter in this segment.
Finally, intelligent reporting and analysis is the new front of QA, where the convergence of automation, AI, and analytics will accelerate the work of quality assurance into a new dimension of efficiency, accuracy, and strategic impact.
Source: Erome.News
