Manual testing is expected to undergo significant transformations in 2024. What can we anticipate in the QA field? How will AI technology impact the landscape? Will manual testing maintain its position compared to automated testing? What new testing tools are about to hit the market? In this article, we've compiled a list of QA manual trends for next year. Let's explore what lies ahead!
In the last few years, we have witnessed the rapid development of the QA domain. The software testing market exceeded USD 47.25 billion in 2023. The expectation of reaching over USD 103.5 billion by 2033 highlights the significant growth in this sector.
Naturally, being a part of the software development process, testing is influenced by general software development trends. Big data, AI, cloud technology, cybersecurity, DevOps, as well as internal QA trends such as test automation, shift-left testing, exploratory testing, and other trends, will drive manual QA in 2024.
Automated testing can handle many tasks, but some parts of testing require a thorough manual approach. The current trend in software testing is all about combining both automated and manual testing.
There are various non-automated scenarios. Additionally, there is a list of testing types where automation can't achieve the same level of efficiency as manual testing. UX testing, UI improvements, exploratory testing, and ad hoc testing necessitate a manual approach with a human touch.
Simultaneously, automation is necessary for browser and OS compatibility testing when we need to ensure that software works as intended on different browsers, operating systems, and devices. Automated testing is also essential for performance and load testing. Unit testing should take top priority for test automation, followed by integration testing and functional testing.
The year 2023 was filled with AI projects, approaches, and techniques. ChatGPT, along with neural networks, can simplify and enhance the work of QA engineers. You can use ChatGPT for generating test cases, writing test documentation, and preparing data. Additionally, AI technology can be applied to automated testing, including unit testing. Furthermore, AI simplifies error decoding and makes your reporting clearer, speeding up the bug recognition and fixing process.
Over the past few years, testing tools have incorporated AI/ML technology. The artificial intelligence algorithms automatically categorize issues based on their potential causes. Moreover, AI/ML can be employed for visual testing, where visual inspection tools using machine learning identify differences between the current UI and the approved design that might be challenging for human testers to detect.
Additionally, AI streamlines test coverage analysis. This technology enables the analysis of current test coverage, highlighting areas with low coverage and those at risk. After making even a small change in the application, it's necessary to run tests to ensure everything functions as expected. While running the entire test suite may be impractical at times, AI/ML allows the identification of necessary tests, thereby saving time.
Throughout the years, testing teams have looked for systematic ways to verify and process data, evaluating it based on parameters like accuracy, relevance, believability, repetition, and the number of records. Nowadays, new testing tools make data collection more practical. This opens up the possibility of witnessing the development of additional tools for testing data, contributing to the creation of high-quality products over time.
Data testing tools come in various types, with two key categories:
Data validation tools. These tools focus on checking if input data meets specific user-defined criteria. They detect issues like missing values, inconsistencies, or duplicates, ensuring the accuracy of results.
Data profiling tools. Profiling is vital for understanding your dataset's structure and content. It provides insights into patterns, relationships, and frequency distributions. This knowledge guides informed decisions for cleaning and preprocessing data before analytics or model training.
Additionally, specialized tools exist for specific domains, like ETL (Extract-Transform-Load) tools for managing data pipelines and integration tools for combining information from diverse sources.
Quality test centers function as dedicated hubs where comprehensive testing activities are conducted to identify and rectify defects, ensuring the delivery of high-quality products. Compared with QA teams, quality test centers are larger, specialized facilities or organizations offering comprehensive testing services. They may function as internal departments within a large organization or external entities providing testing services to multiple clients. Quality test centers may also have a more centralized or dedicated structure, operating as standalone units serving various projects or clients, either independently or in collaboration with different business units.
In general, quality test centers extend beyond typical QA teams. They often offer a broader range of testing services, encompassing test automation, performance testing, security testing, and more. These centers may have specialized teams for different testing types and can leverage advanced tools and technologies.
Quality test centers are becoming an important part of global software testing trends. This shift promotes the development of high-quality software applications over the long term and improves the entire application development process.
In 2022, organizations worldwide detected 493.33 million ransomware attacks. Phishing continues to be the most prevalent cyber attack, with around 3.4 billion spam emails sent daily. The global average cost of a data breach was $4.35 million in 2022, while breaches resulting from stolen or compromised credentials incurred an average cost of $4.50 million.
Thus, security testing has become incredibly crucial. It is essential to ensure that products, networks, and systems are safeguarded against cyber threats and various risks. Moreover, security checks have become a standard practice for transaction processing and ensuring user safety. In 2024, it is anticipated that secure software will embrace improved coding practices.
Penetration testing is an example of a security testing activity. Many IT industries are adopting the practice of penetration testing to identify flaws in software before its release. This testing extends to APIs, microservices, and backend software. The goal is to reduce security vulnerabilities, with a resolution target set for 2024 as part of these ongoing software testing trends.
The worldwide Internet of Things (IoT) market is expected to expand from $662.21 billion in 2023 to $3,352.97 billion by 2030. As a result, in 2024, IoT testing is anticipated to be a significant software testing trend.
This testing approach allows testers to assess and analyze potential risks in IoT devices. The focus of IoT testing will primarily be on ensuring the security, accessibility, compatibility of software, data integrity, performance, and scalability of IoT instruments.
Every phase of software development involves testers, and QAOps is a practice that unites developers, testers, and operations teams. This approach integrates continuous testing with the DevOps methodology, enhancing both the CI/CD process and the testers/QA pipeline
The DevOps Global Market Report for 2022 predicts a substantial growth in the DevOps market, forecasting an increase from $10.84 billion in 2023 to $24.71 billion within the next four years. In 2024, it is expected that more companies will embrace the DevOps approach and incorporate QAOps testing trends.
As numerous IT companies adopt cloud platforms for simplicity, scalability, and reliability, there is a growing trend towards increased virtualization and cloud usage. Infrastructure as Code (IaC) emerges as a prominent software testing trend, addressing challenges related to server allocation and configuration. This technology reshapes the architecture management process, providing a solution to longstanding bottlenecks.
Here are some potential directions for new types of testing tools in 2024:
AI-Driven testing tools. With the increasing integration of AI in software development, there may be new testing tools leveraging advanced machine learning algorithms for test case generation, test execution, and result analysis.
Quantum computing testing tools. As quantum computing gains traction, there could be a need for testing tools specifically designed to validate the functionality and performance of quantum computing applications.
Extended IoT testing tools. With the continued growth of the Internet of Things (IoT), testing tools may evolve to address the unique challenges associated with testing IoT devices, networks, and ecosystems comprehensively.
Decentralized application (DApp) testing tools. As blockchain technology and decentralized applications become more prevalent, testing tools may emerge to cater to the specific requirements of DApp testing, including smart contract testing.
Augmented reality (AR) and virtual reality (VR) testing tools. The rise of AR and VR applications may lead to the development of specialized testing tools to ensure the quality and user experience of immersive technologies.
5G testing tools. As 5G networks become more widespread, testing tools may emerge to validate the performance, security, and reliability of applications in 5G environments.
Edge computing testing tools. With the growing adoption of edge computing, tools may be developed to address the unique challenges of testing applications deployed at the edge, including latency and connectivity issues.
Exploratory testing serves as a response to the challenge of developing quality solutions without heavy reliance on automation. A fundamental aspect of exploratory testing is chartering, which involves the clear definition of a mission for the testing session. Testers act as investigators, asking questions about user stories and setting the mission scope. By turning off distractions and focusing solely on the session, testers can uncover new bugs creatively. The effectiveness of session-based testing is often enhanced when conducted in pairs.
Shift-left testing is anticipated to gain prominence in 2024. This approach involves moving testing activities earlier in the development cycle, ideally to the coding stage. Developers are encouraged to write unit tests and conduct code reviews with a testing mindset. By identifying defects at the source, shift-left testing minimizes the chances of critical bugs progressing to later development stages. It promotes a quality culture within development teams, saving time and resources.