In today's fast-paced software development world, test automation has become an essential practice to ensure the quality and reliability of software products.
This article will discuss the advantages of test automation, including recyclability, receiving footage, scalability, concurrency, discovering issues earlier, secret savings, observing manufacturing processes, subjecting applications to high loads, and profitability.
However, it is also important to acknowledge the limitations of test automation, such as not being able to cover every possible scenario, the need for manual testing for assessing usability, and limited capabilities in identifying crashes under specific circumstances.
Once there is an existing testing script, it can be re-executed on various form factors and devices effortlessly. Additionally, if the test is composed of building blocks or components, those components can be repurposed in other tests. This implies that you only have to write the login functionality once and reuse it.
If your application introduces a new drop-down for "client type" (individual or business), you can modify the login function in a single location, and all the tests will pass smoothly once again. The more detailed the manual testing scripts, the higher the chance that they will become outdated or require expensive maintenance to remain current.
Historically, a successful execution of an automated test merely confirmed the software's ability to run through a particular use case. The computer struggled to detect issues such as visual anomalies or usability complications resulting from a code change. In extreme cases, an entire input field could go unnoticed by the automation, yet still be filled with text. As such, a passing test did not necessarily ensure the feasibility of running the test scenario.
Modern test automation tools provide video capture of not just failed runs but also successful ones. By analyzing these recordings in fast-forward mode (e.g., 2x), human intelligence can complement the machine's repetitive evaluation to identify potential problems. Such video playback can dramatically reduce the time spent on debugging, from hours to mere seconds.
Once a test has been developed for the iOS platform, there are thousands of permutations of devices, operating systems and browsers to explore. The fragmented Android ecosystem offers hundreds of thousands of combinations. For web-based applications that perform equally well on laptops, there are millions of potential combinations. Running all of these at once isn't necessary.
By running a separate test for each code change, only about a dozen tests may be run per day. Choosing the top one hundred combinations and cycling through them during test runs is one approach for gaining comprehensive platform coverage with minimal cost.
Alternatively, selecting the ten most critical tests and running them across one hundred devices overnight can also be achieved without significant expense. Cloud-based testing solutions mean there is no longer a need to choose between minimal hardware with lengthy test runs or constructing a costly test lab worth hundreds of thousands of dollars.
By leveraging the cloud and the ability to repeat tests, it's feasible to execute identical tests on multiple platforms concurrently, searching for subtle dissimilarities. This approach can offer valuable information regarding coding methodologies and performance optimization. For instance, you could compare the test runtime and average webpage response time between an outdated iPhone and the latest model.
It's highly probable for a couple of developers who are collaborating simultaneously to make alterations that might cause the software to malfunction - or result in a conflict during merging that breaks the software. That kind of issue is catastrophic, like "unable to login" level of brokenness.
The human tester detects the problem several hours after it occurs and is informed that it "shouldn't happen", or they are required to wait for a new build. The next day, the tester confronts the developers about the issue, only to be told that it "works correctly in the development environment," or operations are called in. It typically takes four days before the issue is finally resolved.
If your experience has been better than this, you're luckier than most. Alternatively, while it may be infrequent to experience login malfunctions, discovering issues during testing or at least uncovering unanticipated consequences of changes is common.
Having an automated regression test suite executed with each build detects issues early, narrows down what changes caused the problem, and holds the developer accountable for creating it. This approach prevents wastage, delays, finger-pointing, and significantly reduces the amount of time spent on debugging and fixing.
Most of the teams that take the lead in human testing have a "regression test" or a "final check before release" test cycle. This phase can be quite costly, and it may last for up to a week. With such a long testing cycle, it becomes challenging to ship products often. In my years of experience, I have worked with two teams that opted for quarterly releases instead of monthly releases as a "process improvement" strategy.
Reducing the frequency of releases means that the team spends less time developing new code as a percentage of its overall working hours. However, when it's time for regression testing, there will be more significant changes to implement, hence requiring more regression test cycles to finalize the release. This leads to an increase in testing costs, creating pressure to reduce the frequency of product releases, and ultimately creating a vicious cycle from an economic perspective.
However, using modern software engineering tactics such as multiple deploy points, resilience, and quick recovery time, combined with efficient tooling, can drastically transform the numbers. The team can create a positive feedback loop where frequent deployments result in fewer changes deployed, reduced testing needs, and faster customer value.
After a "read-only" testing script has been created, it can be repurposed for monitoring production. By incorporating alerts and notifications, users can be alerted to system failures or the misuse of configuration flags within seconds. To enable performance monitoring of the actual customer experience, users can add timing functionality at a fraction of the cost of a typical lunch hour. Additionally, incorporating alerts to trigger when load times surpass a reasonable threshold can allow for faster identification and resolution of problems.
As an application ages, its logic and database grow in size, eventually leading to decreased system performance. By incorporating timing into the production monitoring process, it becomes easier to detect and address performance issues before customers notice them. This proactive approach can help ensure optimal system performance and customer experience.
Once again, initiate a battery of individual tests that emulate user behavior. Next, execute these tests concurrently at a substantial scale to gain in-depth knowledge about performance. There are two main insights to be gained through this process. Firstly, you can examine the performance indicators that were developed for monitoring production environments as described earlier. Alternatively, human beings can explore the application's functionality in an environment that closely resembles production and is under a similar degree of stress.
If a squad is implementing fresh coding every sprint, then the "area" to "address" with tests will amplify with each sprint. Through automated tools, that work stays relatively steady: Code a tale, code some tests. However, with human testing, the quantity of space to address grows directly. In the first sprint, test 10 stories, in the second sprint, test 20, in the third sprint, test 30. Testing must either become more sophisticated random sampling or testing time needs to extend.
Executed proficiently, testing tools result in diminishing test expenses over time.
The most effective tools for testing automate a small subset of user actions that are frequent or important. These tools run these actions repeatedly, without variation. Attempting to cover every possible scenario is futile. This approach leaves space for minor defects, as well as for issues that irritate customers.
Keep in mind that automated testing checks for conformity to specifications, which are sometimes incorrect or out-of-date. Most businesses employ automated testing to identify glitches during basic, frequent, essential tasks like logging in, establishing a new account, or sending forgotten password emails. This is what automated tests do.
When it comes to crashes that only occur under certain circumstances, manual testing is still necessary. While machines have advanced significantly, the applications of "real" artificial intelligence are currently limited and expensive.
Another thing that automated testing cannot accomplish is assessing the practical usability of the design, such as button placement or app ease of use. Manual, user-friendly testing is still necessary for this.
In conclusion, test automation provides numerous benefits that significantly reduce testing time, increase test coverage, and improve overall product quality. However, it also has its limitations and cannot entirely replace manual testing for certain aspects of software testing. A balanced approach that combines both automated and manual testing is necessary to achieve optimal results in software testing and ensure customer satisfaction.