Outdated and inadequate testing methods hinder businesses from meeting deadlines and running over budgets due to bug-fixing costs at later stages. As software systems become more complex, traditional application testing methods may struggle with scalability and identify and address defects and issues before they impact customers.

Traditionally, testing methodologies kick in after software has been coded. Due to this, they lack a feedback loop for addressing flaws that may have been introduced in the design phases, even before the coding started. Hence it fails to learn and adapt to the customer’s expectations in real-time. Modern QA methodologies need to address the prime directive of faster time to market actively. Hence, it is crucial to investigate, discover, and identify bugs right from the planning stage before they disrupt the entire process and delay product release.

The aftereffects of software testing failure depend on the severity of bugs in the application. Let’s look at some common challenges with traditional software testing failure when compared with modern testing methods.

Traditional software testing lacks a real-time feedback loop. Without a feedback loop, issues identified during the design and development phases may go unnoticed, leading to more significant problems that require extensive rework, thus leading to longer development & testing cycles, and delays in product releases.

In software development, it’s essential to consider the quality of the application being developed. As the manual testing method may not be able to cover every possible scenario, this can result in defects and issues that go undetected. A lack of test coverage further leads to inaccuracies and inefficient performance. Therefore, it’s important to explore modern testing methodologies that incorporate comprehensive test coverage analysis and automated testing to ensure overall quality and improved user experience.

The cost of fixing a software testing failure can vary depending on the stage at which it’s found, the severity, and the complexity. If an issue is found during the initial stages of design and development, it is generally less costly to fix because the codebase is relatively small and less complex.

On the other hand, issues found during the later stages of development may require significant changes to the codebase, and the cost of fixing can be much higher.

Reproducing issues is one of the critical factors in application testing to identify root causes and fix issues quickly. But in manual testing, testers may encounter issues that cannot be easily reproducible, impacting software quality and delivery timelines. Incorporating automation testing can help you in tracking and reporting to streamline the testing process and identify issues quickly, reducing delays and improving application quality.

To stay ahead of the competition and deliver an exceptional customer experience, continuous integration and delivery of new features are a must. However, manual software testing methods may not be compatible with CI/CD workflows, causing delays in development and time-to-market. Above all, the lack of CI/CD processes in testing ultimately impacts efficiency and the software delivery process while maintaining high software quality.

Behavior Driven Testing (BDT) is a modern approach that combines the power of predictive analytics and AI-ML right from the planning stages and can help product managers save time and costly test sprints.

Behavior-Driven Testing (BDT) is an agile software testing method that focuses on the behavior of the software rather than its functionalities. Behavior-Driven Testing was coined by Dan North in 2003 as an extension of Test-Driven Development.

In BDT, test scenarios are built according to the steps the user would take instead of the software functionality. BDT is a collaborative approach that involves product owners, business analysts, developers, and testers to improve software quality and ensure the application meets user requirements.

Unlike other testing methodologies, BDT test scenarios are built in common/ spoken language rather than computer code, in order to make communication easy and effective amongst the stakeholders and development teams. Thus, BDT allows you to bridge the gap between technical and non-technical teams, accelerating the delivery of products and releases.

BDT is the most reliable and effective software testing method that can bridge the gap between technical teams and non-technical stakeholders by using natural language to define the behavior of the software. This further helps in ensuring the software meets business objectives and is easier to understand for all stakeholders.

BDT emphasizes the use of automation to reduce manual testing efforts and improve testing efficiency. Automation testing is faster, more reliable, and can be scaled immediately, making it a valuable method for software testing.

Behavior-driven testing helps to catch defects early in the development cycle, providing immediate and faster feedback to developers. This allows developers to identify and fix defects before they become more critical and disrupt the overall performance.

BDT tests can be integrated into the CI/CD pipeline and run automatically according to the predefined schedule. This enables agility and allows developers to focus on future development and also accelerate time-to-market.

Predictive software testing uses statistical models and historical data to predict the behavior of the software under different conditions. Unlike traditional software testing, this behavior-driven testing approach utilizes historical data to predict future outcomes, such as the likelihood of error occurrence or process disruptions.

The primary goal of predictive testing is to identify potential problems early from the planning stage before they even manifest in the application development process. By analyzing previous data, user behavior, and test results, developers can identify potential problems and bugs that are most likely to occur.

Predictive testing can be used in functional, performance, and security testing to ensure the quality and precision of a software product. This approach compares the results to the predicted outcomes and resolves challenges instantly before they become costlier.

Predictive analytics helps your QA teams to analyze risk levels in the DevOps pipeline, allowing them to focus on potentially severe bugs first. For instance, if a bug is found in the shopping cart algorithm, it should be given high priority and addressed immediately. With the shift-left testing method employed in BDT, you can identify and fix more bugs than with traditional testing, ensuring a seamless shopping experience.

With predictive testing, your agile team can leverage analytics and data mining techniques to analyze potential reasons for shopping cart abandonment even before purchasing a product. This further helps your development and QA teams conduct an audit on the checkout process and retarget shoppers to boost conversations.

Overall, predictive software testing in agile software development allows your developers and QA team to continuously improve the existing code and integrate new features at scale.

In traditional testing, organizations focus on technical and business requirements while ignoring customers’ usage patterns and experience. In predictive software testing, developers and the QA teams assess customer emotions and behavior, allowing developers to focus on different areas to resolve challenges, including compatibility, performance, functional, and security issues.

In behavior-driven testing models, you can create log files and log defects compiling reports that help you learn more about user experience every time a test sprint happens.

The testing team can align test scenarios and identify critical issue patterns that can become vulnerable within an application. This approach further helps your team to revamp the application development process workflow and streamline future testing processes.

More than 90% of organizations spend time and resources to keep up with the demands of faster release cycles. With real-time feedback in behavior-driven testing models, the QA team can know more about user demands, and the data enable them to reach out to the root cause of the failures.

This continuous analysis and evaluation address the failure points throughout the SDLC. Above all, forecasting future application vulnerabilities reduces last-minute glitches as well as the time-to-market.

In the pharmaceutical industry, timely delivery of new features and updates is crucial, while minimizing application downtime and errors. Unfortunately, one of our clients in the pharma industry struggled with frequent builds and deployments due to inefficient manual testing, leading to product release delays and increased bug-fixing costs.

To address these issues, we implemented a robust Behavior-driven agile QA methodology, leveraging the Cucumber tool in the Gherkin language. Our approach included the development of an automation framework and test suites, along with sprint-wise automation suite execution and detailed functional issue reports.

To further enhance our testing capabilities, we introduced data, keyword, and hybrid-driven automation testing methodologies to tackle any issues identified during regression testing. By prioritizing defects and addressing them promptly, we ensured efficient bug resolution and helped our client streamline their processes.

Amzur has been in the QA industry for more than 16 years now. We helped many SMBs across industries develop and deliver top-notch products with high-quality standards. Our customized software QA services ensure your application is accurate and deliver expected results without any process disruptions and downtimes. You can visit our website for more details about our testing and QA services.