Machine learning in software testing is the best thing for QA since test automation. Testing is a crucial part of SDLC and until a few years ago, this was done manually which is often tedious, boring, and repetitive, apart from time-consuming! Software updates used to take a lot of time for release and deployment. This has all changed with the introduction of automation in testing. Automation in testing is a method of software testing using automation tools to execute, maintain test data and generate test reports for further analysis. Automation of testing helped organizations in bringing their software products and updates much faster, saved time, saved money, increased test coverage, and improve accuracy in testing before. This helped QA teams all over to execute more tests in less time and with little effort which in turn helped organizations in bringing faster releases to the market. With artificial intelligence evolving much faster than before and dominating every sphere of technology at present, software testing to has made some giant strides in utilizing AI and ML in software testing. Today, most organizations are using artificial intelligence and machine learning for test automation to speed up their SDLC process. Let’s look at this transition.
Need for automation of testing
The main purpose of testing software is to find out bugs, defects, errors, and ease of using the software besides ensuring the purpose of the software is met. The elaborate process of software testing is done through various tests like unit tests, function tests, integration tests, and performance tests. These tests, which were done manually used to take a lot of time slowing down the software releases. Moreover, as one moves up the testing hierarchy from unit tests to more complex testing like functional testing and regression testing, the process of testing gets more complicated further slowing down the testing process.
This is where automation of testing comes in handy. When automation is done in software testing wherever possible, it not only saves a lot of time but it has various other advantages. Automated tests are more accurate than manual testing as it is impossible not to err by humans. As automated tests follow strict code, and can continuously perform thousands of test cases without being tired or fatigued, there is no chance of errors, especially with repetitive and stress tests. Automated testing also improves the test coverage as these tests can be performed simultaneously on various systems and the logs they produce will give insightful reports. Also, unlike humans, automated testing need not take a break and can work continuously round the clock without affecting the quality of the test performance.
Limitations of test automation
While automation of testing has helped the QA teams immensely in speeding up the software development process, it still has its share of issues and limitations at every stage of the testing life cycle.
To begin with, automated testing is an expensive affair. An automated system requires high capital expenditure to design, fabricate, and maintain than a manual system operated by humans. Automated QA professionals are more expensive to hire, too. For this reason, most low-level to mid-range firms prefer manual testing over automated one.
Even though the automation of testing significantly cuts down the testing time, the creation of tests still takes a lot of time. Developing testing software for the first time takes a considerable amount of time and the creation of test cases still requires professionals.
Automated testing still requires skilled manual professionals to operate and monitor an automated testing system.
The most significant drawback of automated testing is the missing human element. Automated testing can report errors and bugs but it cannot detect certain human aspects such as usability, appearances, and consistency. In today’s digital world and economy, user interface UI, and user experience UX are very vital, and automated testing fails in both these areas.
The above drawbacks have limited the use of automated testing only to a few big firms that can afford the associated high costs and skilled workforce.
AI and ML in software testing
Artificial Intelligence, AI is a simulation of human intelligence by a computer system, and Machine Learning, ML is the development of such systems where computer system learns and adapt themselves without following explicit instructions, by using various statistical models and algorithms to draw inferences from historical data provided to them as input.
Machine Learning in software testing is gaining popularity by the day as it not only eliminates most of the human intervention but also tackles the issue of continuous monitoring testing software updates by constantly generating and updating the test cases to find errors and improves the scope of the code. Let’s find out some of the advantages of applying machine learning for test automation.
With various platforms, screen sizes, and configurations that are available today on various devices, a visual validation test is one of the most important tests for today’s applications – whether web, mobile, TV or computer. Visual testing or UI testing is a test to evaluate whether the application appears the way it is intended to appear to the end user.
Using ML-based visual validation tools will help detect visual bugs in the user interface that manual testing may miss. Visual testing checks various elements on the user interface appear and work correctly when used on a variety of devices, browsers, and multiple environments that affect screen size, OS, and other variables. AI and ML in software testing tools such as Applitools and Percy.io use visual testing mechanisms to help identify the differences between the actual visuals of the UI with that of the baseline visuals provided to arrive at a decision.
Application Programming Interface testing is a type of software testing to verify the communication between two software systems is fine without any errors and follows predefined exchange protocols. API testing verifies and validates the communication between different services and systems by checking the compatibility among them.
With various micro-services and cloud-based services that are available today, API testing is more significant to ensure smooth communication between the various systems. But due to the heterogeneous nature of various systems, API testing is often challenging with regard to parameter validation, test data generation, call sequencing, and the like. These hindrances can be tackled using AI and Machine Learning in software testing. Applying AI and machine learning for API testing can help manage test inputs, generate test cases, and can even help with continuous reporting and feedback.
When code is changed in a software program, the testing team usually performs all the tests to determine how the code change has affected the whole program, which in reality, is not necessary. Using machine learning for test automation can exactly predict how many tests have to be performed when a code is changed.
This is done using trained ML models to learn from existing production data sets and determine whether a test is required to perform after a change in code is made. This will prevent unnecessary test runs and saves time.
Robotic Test Automation
Robotic Process Automation is a software program that performs repetitive tasks without any human intervention. Since testing involves a lot of repetitive testing tasks, like in regression tests, employing machine learning for test automation would result in various benefits like scalability, improved productivity, and low cost apart from codeless testing and accurate outputs. Most of the tests can be automated using AI and machine learning in software testing which saves time, money, and effort.
Automated fetching of webpages through a software process for the purpose of indexing the content is called web crawling or spidering. Using AI and machine learning in testing tools, test scripts can be written to build data sets and create patterns that can be used later to pinpoint any issues and changes in behavior. The obtained results can then be analyzed to ascertain whether the changes are genuine or not.
AI and machine learning in software engineering has helped software professionals and firms immensely in dealing with many bottlenecks that stood in the way of delivering quality software faster to the market. However, before applying AI and machine learning for test automation, one has to be well acquainted with the business processes, test scripts, and scenarios so that the knowledge gained will help leverage the benefits of using AI and ML better.