Automation Testing in 2024: Types, Frameworks & Best Practices

As of 2019, ±23% percent of organization’s annual IT budget is allocated for quality assurance and testing. 98% of IT leaders say automating business processes is vital to driving business benefits, and if the automation growth trend continues, automation solutions will achieve “near universal adoption” in the next 5 years. Using automation in system testing enables the automation of repetitive testing tasks and reduction of human labor, as well as better testing accuracy and increase in test coverage.

Here we investigate what automation testing is, pros, cons, best practices and AI implementation:

What is automation testing?

Automation testing is the process of testing software, bots, and other systems to:

• ensure they meet the development and design requirements.
• check whether the developed system/bot performs the expected processes
• determine that it is bug-free.

There are two types of testing methods:

• Manual testing
• Automated testing

Both approaches rely on executing a specific test process, and checking the correlations and differences between the predicted and actual results of the process, any differences found are outlined as defects.

Manual testing

In manual testing a quality assurance (QA) tester plans and manually implements the tests for all the features of the software/bot. If defects are found, the QA tester will manually fix them and retest the bot performance. The 3 types of manual testing are:

• Black box testing: In this type of testing, the QA tester focuses on the input and output of the software without considering the internal code structure
• White box testing: White box testing is used to verify the flow input and output of the software/bot, where the tester focuses on the underlying code of the solution.
• Exploratory testing: Exploratory testing is widely used in Agile models. This testing type allows the QA tester to create test cases that are out of the box to further investigate the application in question.

Automated testing

On the other hand, automated testing relies on automation tools to execute test cases, report the results, and compare them with predictions and earlier test runs. There are 2 types of automated testing:

• Functional testing: Functional testing aims to verify that all the features of the solution output the designated exits. Similar to black box testing, functional automation tools focus on the inputs and outputs from the user’s perspective of the system, without investigating how the system works.
• Non-functional testing: Non-functional testing aims to test the underlying features of the system, such as performance, usability, security, and data storage. Non-functional testing tools are typically designed to test the readiness of the system to function as the user intends.

Which tests can be automated?

There are 7 types of tests that are a good fit for automation, which are:

API test

API testing is done to ensure that API meets functionality, security, performance, and reliability standard.

For more on API testing, read API Testing in 2022: 3 Benefits & 8 Different Types

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AI-based API testing automation is made possible by Testifi‘s PULSE tool. It enables performance monitoring and can cut testing expenses and cycle time in half. They are working with multinational companies such as Amazon, Vodafone, and BMW.

Smoke tests

Smoke tests, also called confidence tests, sanity tests, build verification tests (BVT), and build acceptance tests, are a type of functional testing, which determines if the developed module is stable or not.

Integration tests

Integration testing (I&T) focuses on analyzing if individually developed modules work together as a whole system. For example, the process of composing an email (one module) and sending it to another email (second module), performing integration testing would be to check whether the sent mail is received.

Regression tests

Regression tests run a combination of functional and non-functional test cases to check if a change or update in an active system has not changed adversely and affected existing features. For example, Old fix regression where the tester checks if a previously fixed bug has regressed to a bad state.

Security tests

Security checks run functional and non-functional tests to check for system vulnerabilities, ethical hacks, risk assessments, and security auditing.

Performance tests

Performance tests check the speed, response time, stability, reliability, scalability, and resource usage of a system under a specific workload. These types of tests are often non-functional.

Acceptance tests

Acceptance tests, also called user acceptance tests (UAT) or end-user tests, check the system’s ability to perform multiple end-user cases and scenarios. These tests are functional and are the final step before launching the system.

What are automation testing frameworks?

Testing frameworks are logical sets of guidelines to create and design test cases. A framework is a collection of practices and tools that are designed to help QA testers execute and run tests more efficiently. There are 5 different types of testing frameworks with different designs, pros, and cons. These frameworks are:

Record-and-playback

Record and playback frameworks, also known as linear automation framework, are the fastest way to generate test scripts. They do not require testers to write a test code or script, instead, the tester records each step in the test (e.g. mouse clicks, keyboard strokes, desktop navigation) and replays the script back automatically to conduct the test. However, scripts developed using record and playback frameworks are not reusable and need to be modified based on user inputs in every case.

Modular-driven

Modular-driven and modular-based testing frameworks require the tester to split the system into individual modules, for each module, there is an independent test script with its own test data. Modular-driven testing is easier to maintain and update due to script independence. However, using a dataset for each module limits the ability to use multiple datasets.

Library architecture

The library architecture framework is based on the modular-based framework, but instead of splitting the system into individual modules, the tester is required to identify similar tasks within the scripts and later group them by function to create a library of system functions which can be called in the testing scripts. Library architecture frameworks allow for function reusability, but same as modular-based frameworks, data is coded into the script requiring changing the script whenever a data change occurs.

Data-driven

In data-driven frameworks, testers do not embed data into testing scripts, but on external databases such as excel or text files. Therefore, a certain function can be reused with different datasets according to need, significantly reducing the number of scripts required to cover all the possible combinations of test scenarios. Nonetheless, data-driven frameworks are complex to develop and require expert skills in the used programming language.

Keyword-driven

Keyword-driven or action word testing frameworks are similar to data-driven frameworks such that the data is separately stored from the script. However, in keyword-driven testing frameworks developers write the code using keywords which reflect specific actions, for example: “clicklink” will order the system to click on a specific link within the GUI. This framework doesn’t require users to have developed programming skills, but the framework gets more complex as the number of new keywords are introduced.

Hybrid

Hybrid testing frameworks combine different methodologies of the previous frameworks to leverage their advantages of and bypass the difficulties.

What are the benefits of automation testing?

Testing is an effortful and time-consuming process which can benefit from automation in several ways, such as:

Reduce time and cost of testing

As the number of new modules grows in the system, the time for testing increases exponentially. Automation testing of new modules and module integrations enables users to create test scripts and record them to be repeated and reused without additional costs. This can also significantly reduce the time of testing, according to guru99, automation testing is 70% faster than manual testing.

Increase testing coverage

Automation allows testers to focus on writing test cases instead of implementing the tests, increasing the test coverage of the system. In addition, automation enables running complex tests in an unattended manner, allowing complex tests which are usually avoided in manual testing, to be completed.

Increase testing accuracy

Automation testing typically produces more accurate results than manual testing due to minimal human intervention. Additionally, the repetitive nature of testing makes it easier for tools to perform than human workers who can make routine errors.

Increase feedback speed

In manual testing, feedback from novel feature testing takes a long time. Automation allows for a faster feedback cycle due to the immediate reports generated. Faster feedback cycles enable developers to understand errors, identify root causes and quickly fix bugs.

Enhanced data insight

Automation testing provides data about errors and performance in an automated manner, as well as detailed insights about the system’s memory contents, data tables, file contents, and other internal program states.

What are the challenges of automation testing?

Some of the bottlenecks of automation testing include:

High upfront investment cost

In addition to tool fees, automation solution costs include automation infrastructure such as test devices, servers, databases, and cloud costs. However, on the long term, automation testing provides a better ROI.

Hiring skilled professional

Organizations may prefer letting test developers manage the testing tools, however, different automation testing tools have different complexity levels requiring programming and code maintenance skills, as well as knowledge of framework design and implementation. Some organizations prefer hiring automated testing engineers or software development engineers in test (SDETs) to manage automated testing tools.

What are the steps for automation testing?

To ensure the best results of automation testing, it is critical to follow these steps:

Deciding which test cases to automate

Automatable tests include cases which:

• are difficult to run manually because they are
  • based on multiple datasets
  • run on multiple hardware or software platforms
  • made up of complex or repetitive steps
• are costly for the business when the case is not correctly completed. For example a check-out flow is critical for an e-commerce company’s revenues and it needs to be tested regularly to ensure its correct operation

Selecting the right automation tool

To select the right automation tool, organizations need to determine the scope of automation which include:

• complexity of test cases
• type of tests and testing
• target of testing (e.g. API, web applications, software, RPA bot, etc.)
• platform for testing (e.g. mobile, desktop)
• resources and datasets used in testing

Based on these you can check out top tools in various categories such as:

• General testing
• Load testing
• Penetration testing
• RPA: RPA solutions use the GUI like a human to perform tasks and are used for writing GUI based tests
• Usability testing

With the tests and your toolset identified, you can follow these best practices to set up testing automation:

What are the best practices in testing automation?

• Test on real devices
•  Distribute tasks according to skills
• Testing one feature per case
•  Provide version call option

For more on testing best practices read Top 10 Best Practices for Software Testing.

Using AI in testing automation

AI/ML algorithms are starting to be used in automation testing, as well as RPA bots to:

• Automate GUI tasks in testing
• Automate script data updates
• Automate test maintenance
• Create low/no-code test cases
• Generate data for testing based on training data
• Test execution and analysis

Different algorithms and AI solutions can target different phases and types of testing. However, it is important to provide good quality data for training the algorithms in order to avoid AI bias and overestimating testing accuracy.

For more on this topic, feel free to read our article AI in Automation: Discover tasks to automate with AI in 2021

For more on AI and automation

If you are ready to deploy automated testing, feel free to check out our data-driven, transparent list of top vendors that can enable testing automation .

If you are interested in investing in AI solutions, check out our data-driven list of data Science / ML / AI platforms.

And if you need help choosing the best tool for your business, reach out to us for guidance:

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.