A product’s performance is the most important goal of a software company. But what if your software fails in production? During development, you need to test your product. Some tests work while others do not. Testing involves the storage of data. Human and artificial data get stored to generate results. Alternatively, artificial intelligence (AI) is becoming popular in today’s world. AI is taking over every discovery by bringing the smartest solutions. As a matter of fact, these solutions have a huge impact on test analysis.
You need to create a testing process through one or more testing frameworks. This is where test analytics comes into play—it produces solutions that are put to the test to compare previous testing frameworks. As a result, a suitable end product acts as the desirable software. But what do we mean by test analytics?
In this post, we will talk about test analytics and the need for testing a software’s performance. We will walk you through the challenges faced in applying test analytics. You will learn about the metrics needed for qualitative analysis. Further, we will brief you on what AI is and how it can help you in qualitative analysis as well. Did we pique your interest? If so, let’s go to the next section to find out all about test analytics.
What Is Test Analytics?
What do you need to test in your software? The answer to the question defines test analytics. Test analytics is a web-based tool that shows test trends. You can consider this to be a replacement for the test plan. To display the test trends, test analytics shows previous test results for comparison. Not only that, you can also get error rate, pass/fail ratio, and certain other parameters using this tool. Also, multiple users can work on this tool at the same time. And the best part? You can use this tool across different products under development.
You need a plan to start testing in the long run. Testing helps in finding risks. You can minimize the risks by finding solutions. Constant software testing has its perks and challenges. But is test analytics necessary? Indeed, it is. Let’s discuss its importance.
What Is the Need for Test Analytics in Software?
How can you reach your company’s goals? Test analytics can help you. You can test the pros and cons of software using test analytics. Meanwhile, you can weigh these pros and cons against each other to produce outcomes, which help in creating solutions. As a result, you can use the solution to analyze other software in the future.
You need test analytics in software because of the following reasons:
Ensuring Quality for Clients
In order to build good software, you should study the market. Hence, you can create better products according to your customer’s needs. In fact, test analytics can produce consumer opinions. This will help you in testing the quality of your product. If the quality is good, more consumers will try it out. Test analytics can capture customer feedback in the same fashion. Can this data detect risks? We will discuss avoiding risks in the next point.
How can you find risks in software? Test analytics can help you detect threats with the help of AI. Data stored in analytics produce this information, which you can use to pay close attention to problems. Test analytics can also reduce or remove risks with the help of AI.
But what are the risks that you can face? Test analytics can be tricky. You will have to face some challenges while testing software. We will learn about these challenges in the following section.
What Are the Challenges Faced in Testing?
Can software run smoothly? In most cases, it does not. There are several challenges you can face in the QA process. Let’s talk about some of them.
Can you do more with less? As you develop software, you will notice that there are many things to do. In agile, customers expect a speedy delivery. You have to satisfy your customers’ needs. Therefore, you have to deliver your tasks on time. This is a major issue faced by most testers and developers in agile. In order to increase the quality of your work, you need to master time. How to do that? Reduce manual work and replace manual activities like regression testing with automated tools.
Data Management Challenges
Where can you store all your data? You need to manage your data efficiently. Loss of data can lead to financial loss. Not only that, if the data belongs to a client, your company can face a lawsuit as well. So, focus more on data security. Also, you must think about complying with data privacy regulations like GDPR. Hence, focus on using synthetic data and data masking rather than using real data.
Too Many Mobile Devices
How many devices do you use? There are many devices in general. While developing a web app, you need to think about multiple operating systems. You also have to consider the device on which your app will run. The real challenge comes during cross-device and cross-browser testing. It’s quite difficult to arrange hundreds of mobile devices, browsers, and operating systems. How will you perform these tests if the budget is low? You can use cloud-based cross-browser testing tools.
With so many challenges at hand, you need to apply strategies. Efficiently planned test strategies help in testing software easily. In the next section, we’ll discuss some metrics you need to consider during testing, which will literally show you the health of the software you are testing.
What Are the Metrics Required for Your Next QA?
How can you avoid risks while testing? You are expected to deliver quickly. In order to achieve no errors, you should establish some metrics for test analytics. These metrics are easy to install, and they will help in making your analysis faster. Let’s get started.
Can you automate tests to reduce risks? Yes, you can. A machine works in a more error-free manner than a human. Check your test cases and choose what you can automate. You should start with a small goal. For instance, if your software needs repetitive testing of a login feature, you can automate it to save time. You can finish regression tests quickly with the help of the latest automation tools. The automated test metrics will give you the count of how many tests are automated and how many are manual.
Count of Defects
You should monitor the defects in software and their count. Monitor the defects and arrange them in categories—open, closed, and in progress. Having an accurate defect count helps management find out if the product is ready for release. Usually before release, companies maintain a count. If the number of low-priority defects is below that count, the product can be released. But if the number is higher, or if any high-priority bugs are still open, the release is postponed.
Defects Per Day
Can you track your defects? This is the most important step. Keep a note of the defects brought to your attention. The number should decrease every day. Hence, you can confirm the efficiency of the team. Get a proper defect tracking tool for your team to get a daily count of detected and fixed bugs.
Do you monitor all your tests? Seeing how you can execute tests is great for analytics. Firstly, you can classify the tests as manual or automated. You can base the results on the test execution.
All these indicators are important for QA. Is there any other metric for QA? One popular metric called artificial intelligence is used in all software these days. In fact, AI is the future of testing and can help in test analytics. In the next section, you will learn about AI and how to ensure software quality through AI.
How Can AI Help in QA?
It is not just the development team’s role to maintain product quality. In fact, everyone in the team has to work hard to ensure quality. Regular tests, data analysis, and suggestions help in reducing threats to software. Can AI help in QA? AI has the potential to work 24/7. It works in the background, making your job easier. How can AI help in test analytics? Let’s find out.
What do you do to view test reports or bug count? Obviously, you check the dashboard of your QA tool. The job of a dashboard is to report data only. But with AI, your dashboards can do a lot more. AI can analyze the combination of OS and browsers. Once that is done, AI analyzes the result from previous tests and finds a failure pattern, thereby helping the tester recognize a trend of test case failure.
Test Case Prioritization
Leveraging AI will not only analyze tests, but it will do a lot more to lessen the workload of testers. As discussed in the previous point, AI works by finding patterns. In test analytics, AI finds a pattern by analyzing buggy tests. Thus, by prioritizing those tests and cutting down review time, AI helps in speeding up deployment.
Create and Maintain Test Cases
Companies usually have a huge load of data. That data contains user interaction recording, log files, A/B test results, and many errors as well. Do you know what AI can do? AI can gather and analyze these data and find patterns. Finally, the patterns can be used to generate new test cases.
When AI tools integrate with RPA, activities like real-time test reporting and test data management are also automated.
It is important to realize that AI already works for some popular apps in the world like Facebook, Netflix, eBay, etc. You can use AI for your company to make work easy. In essence, the use of AI will generate smarter outputs in the future. If you want to know more about how AI helps in fixing quality-related challenges, check out this white paper.
So We’re Saying …
When it comes to developing new software, testing is equally as important as development. The way a testing team works varies in different companies. And in the current agile world, when customers want speedy delivery, don’t you think having a faster testing life cycle will be beneficial for your company? So, why not make more use of automation and try out the latest testing tools?
Give test analytics a try. With a huge load of benefits, especially with AI becoming a game changer, your testing experience will completely change. So, evolve your testing methodology and emerge as a champion by delivering a high-quality product within the deadline.
This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.