Test Automation Statistics for Making the Right Decisions

In today’s fast-changing world, companies have to look both inward and outward for improvements in their software quality practices. Let’s check the pulse of test automation by taking a closer look at statistics related to testing challenges, testing processes, and testing tools.

We collected data from different resources, surveys and research, to help tech leaders, managers, manual and automation testers, and all the teams understand the true power of automation testing, the reasons for demand for testing, and how it opens the strategic potential of becoming genuinely agile.

And looking ahead, the most common answer to the need for test automation is to reduce the cost or time spent on a software release. And while this is just the tip of the iceberg, there are many obvious benefits of choosing automation as companies move from traditional to agile software development approaches.

Table of contents:

• Testing Challenges
• Testing Processes
• Testing Tools
• Software Testing Market
• Top 7 Test Automation Trends
•   Shift-Left Testing
•   Mobile and IOT Testing
•   Codeless Automation Testing
•   QAOps Testing Technique
•   Artificial Intelligence and Machine Learning Testingy
•   Blockchain Testing
•   Agile Automation Testing
• Conclusion

Testing Challenges

Quality assurance testing has various roles, each tailored to specific aspects of software testing. These roles are essential to meet the diverse needs and expertise required throughout the testing process. Let’s look at them in the table below:

Nowadays test stability and test coverage are the most painful challenges for testing teams. 22% of companies mentioned test stability as the most painful challenge for testing teams, and 20% mentioned test coverage.

As the modern apps and software market is constantly changing, in addition to constant browser and OS updates, maintaining sufficient testing coverage is increasingly difficult. And frequent changes also impact test stability.

On the way to achieving quality at speed, there are two major barriers: frequent requirement changes (reported by 46% of companies) and the lack of time (according to 39% of companies).

To improve the software quality and overcome challenges, testing teams want to implement shift-left testing to reduce the impact of the time-consuming aspects on testing. And right now manual testing is the most time-consuming activity in the testing cycle. 35% of companies mentioned manual testing as the most time-consuming activity within a testing cycle according to these software testing statistics.

Companies are prioritizing moving from manual to automated testing as quickly as possible and that’s why moving to automated testing is the top priority around software testing (30% of companies mentioned it as the top priority according to these software testing statistics).

The main advantage of automated testing over manual testing is that it allows you to conduct more tests in less time. This improves performance and extends testing capabilities.

Although manual testing will always be necessary in certain cases, automation will help testers complete more tasks in less time, improve testing coverage and meet test market requirements.

85% of development managers say that it is becoming harder to deliver innovation faster, without compromising on quality and increasing the risk of bugs in production; 90% ​​say that the automation of a large number of test cases may become the single most important factor in their success, as the pressure on IT increases, requiring accelerated innovation.

Interesting software testing statistics: in many companies other team members also take part in the formal testing process. For example Developers, Product Owners, Support Specialists, End Users may take part in this process. 36% of testers indicated that 10-50% of testing is done by non-dedicated testers.

Testing Processes

Adopting some sort of automated software testing (77% of companies mentioned it among different testing practices) and continuous testing (72%) is growing within the industry. The main primary strategic driver in pursuing a test automation strategy is the desire to improve overall quality (55% of companies indicated it).

For 43% of companies, automation is a key part of their quality assurance process, while for 23% it is new and promising.

More than 24% of companies have automated 50% or more of their test cases; 33% of companies would like to automate between 50% to 75% of their test cases, while 21% are aiming to automate more than 75% of their test cases.

Teams integrated automation into more testing activities. Companies applied automation to regression testing (53% of companies), analyzing test results (38%), generating test data (36%), and running performance tests (34%).

Overall, 73% of testers use scripting and/or test automation for functional and regression testing.

While shift-left testing will remain critical to effective functional testing, shifting more aspects of testing left will be especially important in the next few years.

86% of companies follow Agile or Agile-like principles in the development process.

Artificial intelligence (AI) has great potential for automating testing. Technology can reduce or even eliminate the need for human participation in such time-consuming processes as the development and generation of tests, data preparation, test execution, verification, and maintenance of tests.

Still, less than 50% of companies use some AI capabilities for their test automation. AI is mainly used to create test cases and scenarios, generate test data, detect defects, and prioritize and select tests. This indicates that organizations, especially automation tool vendors, continue to improve the implementation of artificial intelligence for testing automation.

38% of testers indicated themselves as an active part of defining and maintaining the CI/CD process, while the CI/CD process facilitates the process of bringing products to market faster than ever before, and ensuring continuous code delivery to production and a constant stream of new features and bug fixes.

Testing Tools

The interest in test automation tools is on the rise. Fewer QA teams are applying open-source tools. Instead, they employ commercial tools to use their time more efficiently on automated testing.

Selenium is the most widely used tool, but its popularity has declined sharply over the last four years, going from 86% in 2018 to 54% in 2020, and 37% in 2021.

And 39% of companies are interested in using codeless test automation solutions.

Testing teams are keeping up with software testing automation trends to deliver a positive return on investment (ROI): more than 60% of companies received good ROI thanks to automation.

72% of companies indicated that their companies allocate between 10 and 49% of their overall QA budget to test automation related expenditures.

The selection of the tool depends on the Application Under Test (AUT). However, according to these test automation statistics, specialists stress having proper training documents and tutorials for the testing tools (51% of companies mentioned it). Functionality and feature-richness are also one of the top-most concerns (48% of companies mentioned it) for tool selection criteria, while 45% of companies count on the tools’ ability to generate good test reports. A similar number of companies (46%) mentioned the level of programming skills required to operate the tool as a key decision-maker.

The most common challenges in applying test automation tools are the lack of skills and experience in tools and frequently changing requirements.

Also 26% of companies indicated finding the right tools for test automation as the biggest challenge that they face.

As for the metrics that best indicate successful test automation implementation, 28% of companies preferred the speed of delivery, and 26% of companies preferred the number of bugs found.

All mentioned challenges are not necessarily essential as automation processes and testing tools have the potential to eliminate the need for advanced skills required for automation and to cope with changing requirements.

It’s worth mentioning that 90% of companies agree or strongly agree that investing in testing transformation is critical to enabling continuous delivery; 45% of companies prioritized partnering with third-party software testing services.

Software Testing Market

In the past few years, the software testing market has witnessed significant changes: test automation has evolved to speed up the time to market for software with the highest testing quality. Automation has always been an eye-catching trend, as it lessens the standard testing efforts and accelerates the testing process.

The automation testing market is expected to grow at 19.2% CAGR from USD 20.7 billion in 2021 to USD 49.9 billion by 2026.

The growth of the automated testing market is driven by the increasing need to implement the transition from manual testing to automated testing.

In turn, the codeless testing market is expected to increase at 15.5% CAGR from USD 1.5 billion in 2021 to USD 6.3 billion by 2026. According to these data, codeless market share in 2023/2022 is expected to be around 6.5%.

Factors responsible for the rapid growth of the codeless testing market include the ease of code evaluation by non-technical team members and the reduction in time that testers spend on repetitive test cases.

Interesting fact
While we were preparing this material, we came across the following software testing statistics: 57% of testers learn testing “Just doing it” 🙂

Top 7 Test Automation Trends

As software engineering strategies become more sophisticated, so do the approaches to test automation. Keeping up with the latest trends is crucial for teams aiming to enhance efficiency, improve accuracy, and reduce time-to-market. These test automation trends reflect advancements in technology and shifts in strategic thinking about how best to integrate testing throughout the development lifecycle. This section explores key trends in test automation, beginning with the increasingly popular practice of shift-left testing.

Shift-Left Testing

Over the last decade, agile methodologies have become more prevalent than traditional waterfall practices, shift-left testing has become the preferred approach to identify bugs early in the software development lifecycle (SDLC).

Rather than treating testing as a final step before deployment, shift-left approaches occur alongside or even within stages of development and design. It involves automated unit tests and integration tests that are developed in tandem with the software itself. Tools that support continuous integration and continuous testing are vital for facilitating a shift-left approach, allowing for automated tests to be run every time changes are made to the codebase.

This early detection helps prevent bugs from making it to later stages, which can be more costly and time-consuming to fix. And that’s not the only one shift-left testing benefit:

• Early bug detection: Bugs are identified sooner, which reduces the cost and effort required to fix them later;
• Increased development speed: Developers receive immediate feedback on issues, allowing for quicker adjustments without disrupting later stages of the project;
• Enhanced collaboration: Encourages constant communication between developers, testers, and other stakeholders, fostering a more collaborative and unified approach to quality;
• Better quality product: Consistent attention to quality throughout the development process leads to a more stable and reliable software product.

Mobile and IOT Testing

The Internet of Things (IoT) has seen exponential growth recently, with smartphones increasingly serving as the primary interface for user interactions. The vast array of product variations, such as different network types (WAN, LAN, and NFC), browsers, application functionalities, loading speeds, performance, security, and user experience presents a complex challenge.

This complexity makes it crucial for developers and testers to automate the testing process on mobile devices. Consequently, IoT automation has become a cutting-edge trend in software testing. Quality Assurance for IoT testing is essential to ensure that these devices and systems perform reliably and securely:

• Enhanced device performance: IoT testing ensures that each device functions as intended under various conditions, which is crucial for devices that are often used in diverse environments and must perform specific tasks accurately;
  
• Improved security: Given the connectivity of IoT devices, security is a major concern. Testing helps identify and mitigate vulnerabilities, protecting user data and preventing unauthorized access or control of the devices;
  
• Optimized user experience: By testing the user interface and interaction workflows of IoT devices, developers can refine the user experience, making it more intuitive and enjoyable for consumers;
  
• Scalability assurance: IoT testing assesses how well a device or system can scale in terms of growing user numbers or data volume, which is critical for ensuring the long-term viability of IoT solutions;
  
• Reliability in varied conditions: IoT devices are deployed in a range of conditions, from domestic to industrial settings. Testing ensures these devices operate reliably regardless of external factors like temperature, humidity, and operational stress.

Codeless Automation Testing

As software development cycles continue to accelerate and the demand for continuous testing grows, codeless automation testing provides a practical solution for teams aiming to scale their testing efforts without the constant need for specialized programming skills. It has the following benefits:

• Increased accessibility: By minimizing the need for coding, codeless automation testing opens up test automation to a broader range of team members, including those who may not have formal programming backgrounds but are knowledgeable about the software’s functionality;
  
• Faster test creation and deployment: Codeless testing tools often come with intuitive user interfaces and ready-to-use actions that simplify the process of test assembly, allowing tests to be developed and deployed at a much faster rate than traditional code-based tests;
  
• Ease of maintenance: Tests created with codeless automation tools are generally easier to update in response to changes in the application they are testing. Non-technical stakeholders can also participate in updating and maintaining tests, which helps distribute the workload more evenly across teams;
  
• Cost-effective: Reducing the dependency on highly skilled test automation engineers can help lower the cost of testing. Codeless testing tools can reduce training time and allow existing team members to contribute more effectively.

QAOps Testing Technique

QAOps is the integration of quality assurance (QA) processes directly into the continuous integration and continuous deployment (CI/CD) pipeline. This approach aligns testing and development operations closely, ensuring that testing is an integral part of the development lifecycle rather than a separate phase.

QAOps emphasizes the collaboration between software developers, testers, and IT operations, fostering a culture where quality assurance is a shared responsibility throughout the development process. This integration helps in achieving faster release cycles, higher quality software, and more efficient resolution of issues:

• Continuous testing: QAOps enables continuous testing by integrating automated tests into the CI/CD pipeline, allowing teams to detect and address issues early in the development process;
  
• Faster feedback loop: Integrating testing into the CI/CD pipeline ensures immediate feedback on the impact of code changes, enabling quick action and adjustments before any code reaches production;
  
• Enhanced collaboration: By incorporating QA into the daily activities of development and operations, QAOps promotes a collaborative environment that enhances communication and reduces the compartmentalization often seen between departments;
  
• Reduced time to market: With testing integrated into the delivery pipeline, the time from development to production is significantly reduced, accelerating time to market without compromising on quality.

Artificial Intelligence and Machine Learning Testing

Testing for Artificial Intelligence (AI) and Machine Learning (ML) systems involves specialized strategies and tools to ensure that these systems perform as expected, are reliable, and make decisions within ethical boundaries. Unlike traditional software, AI and ML systems learn from data, making their behavior dynamic and sometimes unpredictable.

The unique nature of AI and ML applications requires a distinct approach to testing. Effective testing helps mitigate risks associated with AI decisions, prevent bias in ML models, and maintain user trust in automated systems:

• Accuracy and reliability: Testing ensures that AI and ML models generate accurate and reliable outputs, crucial for applications where decision-making impacts real-world outcomes;
  
• Bias detection: Implementing robust testing protocols helps identify and mitigate biases in ML models, ensuring fairness and ethical integrity in automated decisions;
  
• Performance benchmarking: Testing allows developers to benchmark the performance of AI systems under various conditions, ensuring that they meet the required operational standards;
  
• Adaptability and scalability: Continuous testing is essential as AI and ML models evolve based on new data. It ensures that the systems adapt appropriately and scale without degrading performance.

Blockchain Testing

Blockchain testing, which is among the top test automation trends now, involves evaluating and verifying the functionality, security, and performance of blockchain applications. It underpins cryptocurrencies like Bitcoin and Ethereum and is increasingly being used in sectors such as finance, supply chain, and healthcare for secure and transparent transactions.

Given the decentralized and immutable nature of blockchain technology, testing is crucial to ensure that all transactions within a blockchain are validated and that the system is resistant to attacks:

• Security assurance: Testing identifies vulnerabilities and security flaws within the blockchain, ensuring that the system is tamper-proof and secure against potential attacks;
  
• Performance optimization: Performance testing helps determine the system’s scalability and its ability to handle transactions efficiently under different loads;
  
• Functional accuracy: Functional testing verifies that all aspects of the blockchain operate as intended, from smart contracts to the ledger itself, ensuring that every function performs correctly according to the specifications;
  
• Regulatory compliance: For blockchains in regulated sectors, testing ensures compliance with legal and regulatory standards, which is crucial for widespread adoption.

Agile Automation Testing

This approach emphasizes continuous testing to ensure that new features work as expected and do not break existing functionalities, which is crucial for the iterative development and frequent release cycles typical of Agile methodologies.

Agile Automation Testing facilitates continuous integration and delivery by allowing teams to execute tests frequently and efficiently. This integration ensures that software quality is maintained even as updates and improvements are continuously rolled out:

• Speed and efficiency: Automated tests can be run quickly and repeatedly, which is essential in Agile settings where changes are frequent and need to be validated promptly;
  
• Early bug detection: By continuously running tests at every stage of development, teams can identify and address bugs early before they propagate into later stages, reducing the cost and complexity of fixes;
  
• Improved accuracy: Automated tests eliminate the possibility of human error in repetitive testing scenarios, ensuring that tests are precise and consistent;
  
• Enhanced team productivity: Automating routine tests frees up the development and QA teams to focus on more complex tasks, such as new feature development and user experience improvements.

Conclusion

Automation has become a major activity in the last few years as companies look to accelerate software innovation and sharpen their competitive edge. Automation provides significant benefits and also allows companies to efficiently allocate resources to perform more important tasks that move the business forward.

Therefore, it is not surprising that the above test automation metrics show a growing interest in automating testing, as organizations are looking for a way to simultaneously improve the quality, speed, and efficiency of their efforts to bring innovations to market, without fear that higher speed means lower quality.

If you want to try test automation for your web app or website, you can use DogQ – a codeless test automation tool, which can perform regression, e2e, UI, and other types of web testing.

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