2024 Software Testing Trends: QA Technologies, Data, & Statistics
Trends move quickly in software development and software testing. Heading into 2025, it’s hard to predict where quality assurance and testing will be.
With advancements in AI, agile methodologies, and automated testing tools shaping the industry, understanding these shifts helps teams deliver high-quality software efficiently.
Software testing trends and QA data can help predict emerging best practices and methodologies, so we’ve combed the latest reports and surveys to find the most relevant stats for you and your QA team.
Whether you’re leading a team, performing quality planning, or a tester yourself, the software testing trends in this article will provide some insight (or an interesting tidbit to impress your QA friends) into trends you can use in your software testing process.
Software Testing Tool Trends
Software testing tools are essential for testing and quality assurance. They can help testers find bugs and speed up the testing process to launch software faster.
The adoption of AI-powered testing tools has significantly transformed the software testing landscape. Tools like Testim and Applitools have gained substantial traction by leveraging machine learning to enhance test automation and predictive analytics in quality assurance (QA).
Testim utilizes AI to create and maintain automated tests, reducing the time and effort required for test development and upkeep. Similarly, Applitools employs AI-driven visual validation to ensure UI consistency across various platforms and devices.
Beyond these, AI-based alternatives to traditional tools like Selenium have emerged, offering more intelligent and adaptive testing solutions. For instance, Functionize combines AI with cloud-based testing to execute end-to-end tests that are self-healing and capable of running at scale.
According to a 2024 survey by McKinsey, AI adoption in at least one business function has jumped to 72%, indicating a significant increase in the integration of AI technologies across industries. This surge reflects a growing recognition of AI's potential to enhance efficiency and accuracy in software testing processes.
The shift towards AI-driven testing tools is driven by their ability to handle complex testing scenarios, adapt to changes in the application under test, and provide predictive insights that traditional tools may not offer. As a result, organizations are increasingly investing in these advanced solutions to improve their QA processes and deliver higher-quality software products.
10 Top Software Testing Tools for QAs!
Here's my pick of the 10 best software from the 10 tools reviewed.
Software Testing Technique Trends
Without a software testing methodology to guide your work, testing is like wandering aimlessly in the direction of a product launch – you think you’re getting somewhere, but you never get any closer because you can’t see where you are on the path.
In 2024, software testing techniques continue to evolve to meet the demands of faster development cycles and high-quality output.
- Continuous testing within DevOps pipelines has become a staple, enabling teams to identify and address potential issues as early as possible in the software development lifecycle. This approach integrates testing into every phase of the pipeline, allowing for real-time feedback and immediate bug detection, ensuring that code changes are verified swiftly before moving forward. According to recent industry reports, over 80% of organizations practicing DevOps have now integrated continuous testing into their workflows, reinforcing its role as an essential practice for maintaining quality in rapid releases.
- Shift-left testing has also seen substantial adoption as more companies emphasize defect prevention over defect detection. By moving testing earlier in the development process, teams are catching issues at their source—during requirements gathering, design, and initial coding phases. This proactive methodology not only reduces the cost and time associated with late-stage bug fixes but also supports collaborative efforts between developers and QA teams. In 2024, 68% of QA professionals reported incorporating shift-left principles into their projects, marking an increase from previous years as more organizations realize its benefits in maintaining a seamless development cycle.
- Model-based testing (MBT) and behavior-driven development (BDD) frameworks are further driving this shift in software testing techniques. MBT allows for the automation of test cases based on models that represent the desired software behavior, offering a systematic approach to test coverage. This is particularly useful in complex systems where ensuring all user paths are tested is critical. MBT has gained traction for its efficiency in generating high volumes of automated test cases with minimal manual intervention. BDD, on the other hand, continues to grow in popularity for bridging communication gaps between technical teams and non-technical stakeholders. By using natural language constructs to write test scenarios, BDD aligns testing goals with business objectives and user expectations. In 2024, over 60% of agile teams have adopted BDD to enhance collaboration and ensure that software behaviors align closely with user needs.
These trends collectively illustrate the shift toward more integrated, collaborative, and automated testing strategies that prioritize early detection and comprehensive validation, empowering teams to deliver better software, faster.
Software Development & Testing Trends
Software testing is intrinsically linked to software development, with trends in development directly shaping testing practices. These insights offer critical context into developer workflows and priorities.
In 2024, the integration of generative AI into software development has significantly transformed coding and quality assurance processes. Large language models (LLMs) like GPT have become instrumental in automating code generation and bug detection. Notably, Google's CEO, Sundar Pichai, reported that over 25% of the company's new code is now generated by AI, underscoring the technology's growing influence.
These AI models assist developers by writing test cases and automating repetitive QA tasks, enhancing efficiency and reducing human error. However, the adoption of AI in coding has raised concerns about the security of automated programming tools, as they may inadvertently introduce vulnerabilities into production-grade code.
Concurrently, the emphasis on security testing has intensified due to the escalating complexity of cyber threats. Organizations are increasingly integrating security measures into their Continuous Integration and Continuous Deployment (CI/CD) workflows to safeguard software integrity.
The 2024 State of CI/CD Report highlights that incorporating security tests within CI/CD pipelines is now considered essential for maintaining robust deployment performance. This integration ensures that security vulnerabilities are identified and addressed early in the development process, reducing the risk of breaches and enhancing overall software resilience.
These developments reflect a broader trend towards embedding AI and security considerations into the core of software development practices, aiming to produce more reliable and secure applications in an increasingly complex digital landscape.
Quality Assurance Team Trends
In 2024, quality assurance (QA) teams have evolved to adopt more dynamic and hybrid roles that blend manual and automated testing responsibilities. As agile and DevOps practices continue to mature, QA professionals are expected to be proficient in both traditional manual testing techniques and modern automated frameworks.
This hybrid approach enables teams to leverage the benefits of human insight in exploratory testing while utilizing automation for repetitive and large-scale test cases, improving overall efficiency and coverage.
The 2024 World Quality Report highlights that 72% of QA teams now integrate automation into their workflow alongside manual testing to enhance their capacity to deliver faster and more reliable results.
The shift towards cross-functional teams has been a significant trend as well, driven by the need for seamless communication and collaboration among developers, testers, and operations staff. This integrated approach ensures that quality is embedded throughout the development lifecycle, aligning with the “shift-left” principle that aims to identify and address issues early in the process.
Post-pandemic shifts have also reshaped the composition and operation of QA teams. Team structures have become more fluid with the widespread adoption of remote and hybrid work models.
Recent industry data indicates that 49% of teams operate in hybrid settings, combining on-site and remote members. This has led to the increased use of advanced collaboration tools such as Jira, TestRail, and cloud-based testing platforms that support real-time test management and reporting.
Furthermore, while smaller QA teams (1-5 members) remain common in many organizations, larger enterprises are expanding their QA departments to include specialized roles in automation, security testing, and AI-based analytics, reflecting a broader investment in comprehensive quality assurance strategies.
This shift underscores the necessity for QA professionals to adapt to new tools and collaborative practices to maintain productivity and coherence in distributed work environments.
Quality Assurance Job Trends
Quality assurance covers many angles, and the boundaries between where development ends and software testing begins can be blurry. If you’ve found a quality assurance plan that works for you, your team, and your company, consider yourself lucky.
If you’re reading this, there’s a good chance that you’re either working in QA or you have a strong interest in the field. If you’re the latter and you’re looking for software testing jobs, there are a few things to know before you get started.
- Emergence of Specialized Roles:
- The demand for AI/ML QA engineers has increased significantly, driven by the adoption of machine learning algorithms and AI-powered tools in QA processes. These roles involve ensuring the accuracy and reliability of AI models and developing test strategies for AI-integrated applications.
- Data analyst roles within QA teams are becoming more prominent as companies shift toward data-centric approaches. QA teams now rely on data analysts to process and interpret testing data, enabling more informed decision-making and predictive insights for software quality.
- The role of the DevTestOps engineer has also gained traction, blending development, testing, and operations to support continuous testing in CI/CD pipelines and ensuring seamless integration of testing in development workflows.
- Upskilling and Education Trends:
- In 2024, there has been a notable uptick in QA professionals investing in upskilling. Many QA specialists have enrolled in courses focusing on AI-driven test automation frameworks and machine learning basics to stay competitive.
- Online learning platforms have seen a surge in courses related to Python for automation, machine learning for QA, and advanced test scripting with tools like Selenium, TestNG, and Cypress. This reflects a broader industry push towards automation and AI proficiency.
- Certification programs in AI-based testing methodologies and data analytics have also become popular among QA professionals, indicating an industry-wide emphasis on building expertise that aligns with the latest technological advancements.
- Cross-functional Skill Requirements:
- Communication and cross-functional collaboration skills are increasingly essential. QA job postings in 2024 emphasize the ability to work closely with developers, data scientists, and product managers, highlighting the value of versatility in modern QA roles.
- Many organizations now prefer QA professionals who can transition seamlessly between manual and automated testing, leading to a rise in courses and training programs that cover both aspects comprehensively.
These trends illustrate the evolving landscape of QA jobs, underscoring the importance of adaptability and continuous learning as the sector moves toward more sophisticated, technology-driven processes.
Software Bug Trends
Software bugs are a developer’s worst enemy, which makes testers a developer’s friend (the enemy of my enemy is my friend and all that). Testers do the hard work of testing, retesting, and testing again to find software bugs, which saves developers time and effort.
- Increase in Reported Vulnerabilities:
- The use of open source components in software development has risen sharply, contributing to a corresponding increase in reported vulnerabilities. Many software projects integrate open source libraries and frameworks. This widespread adoption has led to a spike in security-related bug reports, as these components may introduce vulnerabilities not present in proprietary code.
- The Open Source Security Foundation (OpenSSF) reported that the number of vulnerabilities disclosed in open-source projects increased by 35% compared to 2023, reflecting both the higher usage and improved reporting mechanisms. This trend emphasizes the importance of implementing rigorous open-source security audits and maintaining updated vulnerability databases.
- Tools supporting real-time monitoring and advanced analytics for bug tracking have become essential in modern QA practices. Platforms like Sentry, DataDog, and Splunk have enhanced their analytics capabilities to provide QA teams with immediate feedback on code performance and errors, facilitating faster detection and resolution.
- Importance of Real-Time Monitoring and Bug Tracking:
- QA teams now rely more on tools that provide advanced analytics and automated alerts to detect anomalies in real time. Real-time bug tracking platforms that integrate with development environments enable teams to spot and resolve issues before they reach production.
- The demand for AI-powered bug tracking tools that utilize machine learning for predictive analysis has increased. These tools can identify potential areas of code prone to bugs and recommend preventive measures, helping teams prioritize critical issues.
- Bug Resolution Statistics:
- The average time to resolve bugs in 2024 has improved, especially within organizations that adopt continuous integration and continuous deployment (CI/CD) practices. High-performing DevOps teams resolve bugs much faster than those not using CI/CD methodologies, typically within 24-48 hours for high-priority issues.
- The most common types of bugs in 2024 include dependency-related vulnerabilities, stemming from open source components, and configuration errors introduced during rapid development cycles. These bugs are often addressed quickly in environments with automated testing integrated into CI/CD pipelines.
- Security bugs remain a top concern, and most reported issues in QA are related to security vulnerabilities. This trend underscores the importance of embedding security testing early in the development process to catch these issues before they progress to later stages.
To stay ahead, QA teams must adopt advanced bug tracking tools, integrate real-time monitoring, and focus on maintaining secure, up-to-date dependencies to mitigate vulnerabilities effectively.
Aside from testing for software bugs, testers also carry out database testing, an entirely different but still vital process.
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What's Next?
Attempting to create a QA plan or strategy without data can be difficult—so don’t! The trends I covered in this article should provide a good base to get started.
Quality assurance skills are ever-changing—stay alert to catch those bugs and catch them early. Subscribe to The CTO Club's newsletter for the latest software QA tips and best practices!
