Addressing the Common Challenges in Continuous Testing

Published: 2021-10-07 01:36:43


To stay ahead in the competition, enterprises have started adopting agile and devops. In an attempt to push their products to the market before their competitor does, enterprises have squeezed the development and testing cycles, making testing the biggest bottleneck of a software development lifecycle. There is no iota of doubt that <a href="https://www.opkey.com/oracle"> continuous testing </a>
 is essential for continuous delivery, however, making it a reality is a hard part. In this article, we’ll discuss what common challenges enterprises are facing while implementing an effective <a href="https://www.opkey.com/Oracle-EBS-Automation"> continuous testing  </a>
 practice. We’ll also discuss how to address these challenges.

<B>Why is Continuous Testing turning from “nice to have” to “absolute mandatory”?</B>

In a scenario when software release cycles are shrinking from years and months to weeks and days, testing practices also need to evolve to support continuous integration and continuous deployment. So, enterprises need to transform their testing approaches to ensure that apps are operating as intended. Any break will cost you revenue, reputation, & profit. Continuous testing is all about testing early, often, & comprehensively. However, enterprises are struggling to implement it due to given below reasons.

<B>Inadequate Test Coverage –</B> When you start with continuous testing, the very first problem that you might face is requirement gathering. In most of the cases, requirement gathering is still a manual process. It means that testers are still writing test cases based on guesses how customers are interacting with the application. Not only, it is time-consuming but cannot be accurate most of the time. Manual approach for requirement gathering and test authoring usually has gaps in test coverage or contains too many over-lapping test cases.

Test Data Management – Imagine a scenario in which you need to test an ecommerce app. To execute this, you need at least 3 sets of data i.e. personal data (incl. Name, Family name, etc.), address data (to deliver the goods) and payment data (credit card, PayPal token or bank account data or other). In most of the cases, QA teams do not have access to such sensitive information. Another scenario is that in which data is spread across multiple databases. So, it can be painful and time-consuming to extract data for continuous testing.

<B>Lack of Standard Tool –</B> In most of the cases, QA teams use different tools i.e. combination of open-source and commercial off-the-shelf products to validate the code. Often, these tools neither offer proper documentation nor contribute towards test script maintenance. Rather than supporting continuous testing, these tools add to the woes of the testing team. Often, these tools don’t support complete technology stack, making testers juggle from one tool to another rather than focussing on testing.

<B>What enterprises should do to successfully implement continuous testing?</B>
To successfully implement continuous testing, enterprises need to invest in right automation tools that are built to support continuous testing. And, the very first step to find the right automation tool starts with finding a test automation approach that supports the various technologies involved in your business processes. The approach should not only be adopted easily by your existing team members, whatever their current skill set may be but also address the challenges such as test recommendation, test maintenance, test coverage, & test data.

<B>Our proposed approach –</B> Risk-based Continuous Test Automation
We recommend risk-based continuous testing approach. This approach helps you to identify test cases that need to be run during the code-change or application update. Rather than running the entire test suite for any small change in the application, risk-based approach recommends test cases based on change impact assessment. With this, enterprises can cut the tests which they don’t need and increase the risk coverage of their application by >90%.

Change impact assessment is a crucial part of risk-based continuous testing approach. It is considered as the ability to identify which of the critical business functions are impacted during the application upgrade or code-change. Change impact assessment helps QA teams to understand how changes have impacted the current configurations and processes. This helps IT managers to make an informed decision on release maturity. By comparing baseline and updated instance, change impact assessment helps you identify the average testing scope.

Risk-based continuous testing approach also alleviates the “maintenance burden” as changes automatically synchronize across the impacted test cases using Self-healing capabilities. The automation scripts get healed up automatically without requiring human intervention. Issues related to test authoring and test data management ca also be addressed as self-configuring engine automatically adds the configurations and relevant data combinations into the test scripts to enable Risk-based test executions.

Thus, we can say that risk-based testing approach can help enterprises to achieve 90% of test coverage along with 60%+ saving in time and efforts for maintaining scripts in case of any change or upgrade.

The proposed solution should
mimic the actions of the users on the app to reduce test script design time and efforts. Should cut the noise and recommends minimum tests for a change to reduce testing cycles.
identify changes in the object property (Name, ID, Xpath, CSS etc.) and automatically heals automation scripts without requiring human intervention to make script maintenance effortless.
highlight the risk-based areas during application upgrade, enabling QA teams to understand how changes will going to impact business continuity.

mine ERP transactional logs to identify configurations and produce relevant test data sets based upon particular configuration, helping QA teams to concentrate on validation and not on data.

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