Testing has always been a vital part of the software development lifecycle (SDLC), but the approach to testing is undergoing fundamental changes. One of the most significant trends is leveraging Agile methodologies to enhance traditional testing methods.
The evolution of Agile
Since its inception in 2001, Agile has evolved from the rigid waterfall model to iterative methodologies like Scrum and Extreme Programming (XP), effectively removing many bottlenecks in software delivery.
Agile’s popularity continues to grow, with 84% of teams using it according to the 2023 State of Agile Report. Organizations adopt Agile to handle rapidly changing requirements, solidifying its position as the primary development methodology in the industry.
Today, Agile focuses on optimizing the development process by integrating testing from the start of the SDLC to catch defects early and using data-driven insights to enhance efficiency.
“Collaborative testing practices that occur continuously, from inception to delivery and beyond, supporting frequent delivery of value for our customers. Testing activities focus on building quality into the product, using fast feedback loops to validate our understanding. The practices strengthen and support the idea of whole team responsibility for quality.”
Lisa Crispin and Janet Gregory
In the Agile methodology, testing is embedded directly into the development process and is a collaborative effort between testers, developers, and product owners. Because Agile runs continuously with development, it facilitates easier identification and isolation of problems, especially as a dynamic product evolves over time.
This level of collaboration between QA and development—in tandem with shorter feedback loops— allows problems to be found sooner and remain more top-of-mind, which in turn allows developers to fix problems faster before they compound.
Curious about what Agile testing is NOT? Watch our video What Agile Testing is NOT to learn more about common misconceptions and how to avoid them.
The Agile Manifesto
The Agile Manifesto, which serves as the foundation of Agile, consists of four central principles. Here they are broken down into how and why they are relevant for testers:
Image source: Understanding Agile Testing Methodology and 4 Agile Testing Methods
To fully understand agile testing today, it’s helpful to explore foundational agile testing trends that will continue to impact all stages of the development process.
Trend 1. DevOps
DevOps integrates cultural philosophies, practices, and tools to accelerate the delivery of applications and services while ensuring greater reliability. By incorporating Agile principles, such as enhanced collaboration between development and operations teams and increased automation, DevOps empowers organizations to better serve customers and stay competitive in the market.
The 2023 Software Testing & Quality Report highlights the growing influence of DevOps in the software testing market, with an increase in adoption compared to previous years. Particularly when combined with Agile methodologies, DevOps aligns with the overarching goals of agile culture, facilitating the delivery of high-quality software applications.
What started as a small movement has now become a de facto standard in organizations that want a competitive edge. The global DevOps market will grow 18.3% to become a $20.31 billion property by 2028. As teams embrace DevOps, they are increasingly implementing seamless continuous integration/continuous delivery (CI/CD) pipelines, leveraging automated workflows, and adopting active production monitoring to support DevOps practices.
Though these practices help to streamline and simplify the deployment processes, combining Agile and DevOps also requires a shift in organizations to have a more holistic vision of each step of a project and its processes.
Trend 2. Advanced CI/CD pipelines
Continuous Integration and Continuous Delivery (CI/CD) is a practice where software changes are continuously integrated and deployed or delivered. It’s more than just a set of tools—it’s a cultural shift that helps development teams deliver software more frequently and reliably.
With CI/CD, teams can release software updates regularly, keeping pace with evolving requirements and monitoring needs. The pipeline’s flow makes it easier to detect and fix bugs, while smaller, incremental updates enable more efficient testing. CI/CD also allows for easy adaptation to new requirements, enabling developers to implement changes in subsequent iterations.
While current CI/CD pipelines primarily rely on scripts, advancements, like AI, will require additional hardware resources and computing power to manage large amounts of data. Despite initial investment costs, the insights gained from this data will be invaluable.
A well-established CI/CD pipeline is essential for any development team aiming to deliver high-quality software quickly. Testers play a crucial role in ensuring the pipeline’s effectiveness by understanding testing schedules, reporting protocols, and the requirements for each test cycle. As technology advances, adopting modern practices and tools becomes increasingly vital for sustainable growth.
Trend 3. Continuous testing
Continuous testing (CT) is a software development process in which applications are tested continuously throughout the entire SDLC. By evaluating software quality continuously, CT enables teams to identify and address issues early, leading to higher-quality and faster deliveries.
To implement continuous testing effectively:
- Start testing early: Begin testing as soon as possible in the SDLC to catch defects early and mitigate risks. Teams have already found value in starting testing as early as possible and aligning with the shift-left paradigm.
- Embrace the shift-left paradigm: Shift testing activities earlier in the development process to detect issues before they escalate, reducing rework and improving overall efficiency.Continuous testing promotes early and frequent testing in the SDLC, signaling a shift towards proactive quality assurance.
- Automate testing: Automate tests to be triggered automatically at each stage of development, including unit, API, UI, and security tests. Utilize various testing tools available to support these efforts and streamline the testing process. The rise of automation tools for continuous testing streamlines processes, enabling automation of various tests and accelerating feedback cycles.
Image source: Continuous Testing in DevOps
Ways to measure continuous testing:
Image source: DevOps tech: Continuous testing
Trend 4. Shift-left
Shift-Left Testing is all about testing earlier in the software development lifecycle, right from the design and coding phases. This means testers and developers team up right from the start to catch issues as soon as possible. By taking a proactive stance, defects are caught sooner, leading to cost reduction and fostering a culture of quality throughout the development process
This shift aligns with Agile principles of continual enhancement and is part of a broader trend in the industry to build quality into projects from the get-go, rather than fixing bugs reactivley towards the end of the cycle.
Why shift left?
- Cost and time savings: Identify and address issues early to save resources
- Risk mitigation: Reduce the risk of major errors or oversights
- Quality improvement: Maintain a focus on quality for higher customer satisfaction
- Proactive problem solving: Anticipate and address challenges before they escalate
When to shift left:
- Project initiation: Establish a testing-focused approach and identify risks early
- Throughout development: Test at design, coding, and prototyping stages iteratively
- Before code implementation: Validate assumptions and minimize costly errors before implementation
This methodology has gained momentum in agile teams because it helps them deliver top-notch software more efficiently. It’s all about spotting and fixing issues early on, which is crucial for delivering great results.
Trend 5. AI and machine learning
According to the latest annual McKinsey Global Survey on the current state of Artificial intelligence (AI), one-third of survey respondents say their organizations are using gen AI regularly in at least one business function.
AI, encompassing everything from rule-based software to self-driving cars, is defined as any system that mimics human thinking by understanding its environment and taking actions toward achieving a goal.
In software testing, AI and Machine Learning (AI/ML) significantly enhance automation, easing the burden of repetitive testing tasks. While AI/ML cannot replace manual testing entirely, they can supplement and accelerate existing automation systems.
How AI/ML Enhances Software Testing
1. Intelligent code assistance:
- AI can detect, learn, and suggest different coding styles and reusable components, enabling developers to write code faster.
- AI can quickly scan code for insights such as refactoring opportunities, code vulnerabilities, and adherence to coding standards.
2. Automated test generation and execution:
- AI/ML-enabled tools can automatically create, author, and execute tests, improving test coverage and efficiency.
- Self-healing mechanisms in AI tools can proactively identify and fix issues before they impact the system.
3. Enhanced QA processes:
- AI can analyze code changes and identify areas needing more rigorous testing, ensuring higher quality releases.
- AI/ML can monitor millions of data points flowing through the DevOps pipeline, extracting key insights to help stakeholders make informed decisions.
4. Improved decision making:
- AI/ML can refine data from testing processes into actionable takeaways, helping teams optimize their testing strategies and workflows.
- This leads to faster, leaner, and more intelligent pipelines, improving overall productivity and software quality.
5. Predictive analysis
- AI/ML can predict potential issues before they occur based on historical data and patterns.
- This allows teams to proactively address problems and improve the stability of the software.
According to the third edition of the Software Testing and Quality Report, when respondents were asked “How do you incorporate AI into your quality assurance processes?” 22% of teams responded that they are currently leveraging AI to write test cases or scenarios, while 19% use it to create test automation scripts, demonstrating an open-mindedness within the industry towards AI’s potential to streamline and enhance testing.
A smaller but noteworthy fraction of respondents (14% and 12% respectively) also noted that they are employing AI for the management of test data and debugging of test code, indicating a recognition of AI’s capability to address more nuanced aspects of modern software testing.
Recent Trends and Predictions:
- Increased investment: More companies are investing in AI/ML technologies to stay competitive. IDC predicts that by 2025, 75% of enterprises will use AI for more efficient and automated testing.
- AI-augmented tools: The rise of AI-augmented tools is becoming more common, integrating seamlessly with existing CI/CD pipelines and enhancing their capabilities.
- Adoption in DevOps: There’s a significant uptake of AI/ML in DevOps practices, with AI being used to automate and optimize various stages of the software development lifecycle, from planning and development to testing and deployment.
Interested in learning more about how other QA engineers, testers, SDETs, and more, from 30+ industries and 100 countries around the world are taking on testing and QA today? Download the third edition of the Software Testing and Quality Report for free now!
Implementing AI/ML in Your Testing Processes
Structured testing approach:
- Begin by making your testing processes more structured and well-defined. This foundation will support the integration of AI/ML technologies.
- Use AI tools to supplement existing manual testing efforts, rather than trying to replace them entirely.
Invest in training and tools:
- Educate your team on the capabilities and best practices of AI/ML in testing.
- Invest in AI/ML-enabled testing tools that align with your specific testing needs and workflow.
Continuous improvement:
- Regularly evaluate the performance of AI/ML tools and adapt your strategies based on the feedback and data collected.
- Stay updated with the latest advancements in AI/ML to continuously improve your testing processes.
By integrating AI/ML into your testing practices, you can meet growing customer demands and enable rapid release cycles, delivering high-quality products faster than ever before. Embracing these technologies will not only enhance your current processes but also position your team to leverage future advancements in AI/ML.
Curious to learn more about strategies in agile software testing? Check out this free TestRail Academy course on agile testing to help you streamline your agile testing adoption today!
