According to MarketsandMarkets forecasts, the global automated testing market will grow from $20.7 billion in 2021 to $49.9 billion in 2026. The domain is developing rapidly, and this is impressive. However, in order to keep up, QA teams should be aware of many details. What trends will directly affect automated testing in the coming years? We found out in this article.
Testing is a part of the development process, thus, general development trends affect it as well. Let’s see how key development tendencies influence automated testing.
Cloud technologies adopt testing as a service
Cloud computing has changed the way we do business. This is despite the opinion of the experts, that currently many companies are only superficially familiar with the possibilities of cloud technologies. International Data Corporation (IDC) expects cloud infrastructure spending will reach $133.7 billion by 2026.
One of the key trends in the development of cloud computing in recent years is multi- and hybrid cloud environments when enterprises do not tend to stick to one vendor and get the most out of each cloud solution.
Cloud technologies in testing provide many opportunities and advantages. The most basic is the ability to rent the use of physical devices and access them through the cloud to test on real devices. Cloud service models help enterprises adopt testing as a service. At the same time, the company does not need to invest in a testing laboratory, tools, and infrastructure.
AI saves time for testing
In this trend, we will consider two directions. The first is related to AI products and their testing.
Every year more and more products with built-in artificial intelligence technology and its branches (machine learning, natural language processing, natural language understanding, optical character recognition, deep learning, and others) appear on the market. All of these products need testing before release, and so far this is considered quite a challenge. QA engineers must understand AI technologies and create test cases depending on the AI model. Testing products with AI technology involves working with a large data set divided into training and testing data. Experts predict that in the next 3-4 years there will be new tools for testing AI products that can generate massive data sets.
Here we smoothly move on to the second direction, we mean smart tools for test automation testing.
Lots of companies are automating testing using artificial intelligence technologies. The development of test automation tools with embedded AI/ML technology remains one of the trends. This saves time for the QA team: the model automatically classifies problems detected during testing and, based on the analysis of the causes, assigns them a certain status. The engineer no longer has to spend hours trying to figure out the cause of the failure, the system does it by itself.
Software delivery acceleration moves testing to early development stages
In order to speed up the app production and ensure its highest quality, most companies have already switched to Agile and DevOps methodologies of development. These methodologies involve a continuous process of discovering requirements and improving the product through collaboration among self-organized cross-functional teams.
Rapid application delivery requires identifying application bugs and potential risks as early as possible. Accordingly, testing is included in the product development process much earlier. The spread of shift-left testing illustrates this trend.
The etymology of the term comes from the fact that in English, as in most western languages, we read from left to right. Accordingly, if all stages of software development are arranged sequentially, testing is moved from end to start – to the left.
Shift-left testing is at the heart of continuous testing. It involves testing from the early stages of product development. The goal of this approach is to avoid the situation where the discovery of a critical base during deployment requires code correction or even application re-design. Shift-left testing involves early and frequent testing, which will avoid problems in the later stages of development. QA engineers participate in reviewing requirements. Thus, shift-left testing can be carried out even before the code is ready.
As technology advances, so does cybercrime. All efforts to create a quality product that meets the requirements of the end-user will go to waste if the application is vulnerable to cyber-attacks.
Security testing helps QA teams find all possible gaps in the developed software, which cybercriminals may be interested in after the release of the product. The most common types of security testing are:
- Vulnerability scanning is when the system is checked for the presence of known vulnerability signatures.
- Security scanning, which includes the detection of network and system weaknesses. It is done manually and with the help of automation tools.
- Penetration testing, that simulates a hacker attack.
- Risk assessment is when security risks in an organization are analyzed.
- Security auditing, that involves checking applications internally for security flaws.
Security testing approach minimizes the risks of information leakage, and loss of income and resists reputational vulnerabilities. According to Gartner, worldwide security and risk management spending exceeded $150 billion in 2021.
The big data testing process involves examining and testing the functionality of big data applications. This approach requires special tools and techniques. Despite the complexity of conducting such testing, it has a positive effect on business strategy and marketing tactics.
Several types of testing are used to test the characteristics of big data. Among them, we highlight:
- visualization testing, which is used to understand the amount of data;
- ecosystem testing, the purpose of which is to confirm the validity of the data;
- migration testing, that confirms data transfer speed;
- performance and security testing, that are used to check various data.
There are several internal trends in testing that influence the strategies of the domain players.
Autonomous testing means fast generation of test cases, their execution, and analysis of results without human intervention. The autonomous testing platform uses AI/ML technology to automatically detect defects in the application under test. Autonomous testing can also be defined as a learning model that is capable of doing the following:
- to self-adapt;
- to help the QA engineer create tests by predicting what they want to test;
- to ensure that there is a certain correlation between the model and tests so that they can autonomously update each other.
According to Omdia research, 90% of surveyed companies will have fully rolled out autonomous testing by 2024.
The general trend towards accelerated delivery forces QA teams to optimize the testing process. The goal is to save time and make sure that the product reaches the market with the highest quality.
The testing process includes planning, developing, and executing tests. The planning phase includes the creation of documentation with a detailed report on the scope and approach to testing. During the test development phase, a QA team creates a set of tests (test scenarios and test cases). The test execution phase includes executing tests and processing their results.
Experts predict the convergence of tools for various stages of software testing. Using AI/ML technology, all three stages of test management activities will be automated. As a result, this will simplify the entire testing process and speed up the release time.
Software testing tends to multifunctionality
There is a general trend towards the multifunctionality of testing tools. In 2021, several major players in the automated testing market have acquired startups that develop performance testing tools. Thus, large companies have further expanded the range of their services and solutions. We watched as Tricentis acquired Neotys in 2021 and Tx3 Services and Testim in 2022 as well as Perforce acquired BlazeMeter.
These cases indicate that the leading companies in the market see the need for full pipeline automation. In addition to functional automation, this also includes security, availability, and performance testing.
