Overcoming Top Challenges with In-Sprint Test Automation

In-sprint test automation is essential for agile teams aiming to deliver high-quality software quickly and efficiently. However, integrating automation seamlessly within fast-paced sprint cycles comes with its own set of challenges.

This article explores the primary challenges teams face in in-sprint test automation and offers insights on how to overcome them to ensure smoother, more effective automation practices within workflows.

What is In-Sprint Test Automation?

In-sprint test automation refers to the practice of testing the business requirements of an application feature within their development sprint. In other words, all aspects of the feature, whether business-relevant or technical, are verified for optimal performance within a single sprint, leaving no test backlog.

The intent is to deliver each functionality, in its entirety, within a single sprint. Since sprints only tend to last a couple of weeks, it is not possible to implement comprehensive test coverage manually. Automation testing is the only way to run enough tests on each feature and improve test coverage so as to be assured of its production readiness. Regression test suites are also updated with every sprint to ensure that changes and new code have not disrupted existing functionalities.

To achieve optimal levels of in-sprint test automation, consistent collaboration must be set up among all involved teams – BA, Scrum Master, Development, and QA. In this case, software quality must be reoriented as the entire team’s responsibility, rather than just being owned by QA.

The process of automating in-sprint tests can be somewhat complicated, especially when a team is just beginning to build it into their CI/CD pipeline. BAs have to create and define user stories specific enough that developers and testers can create code from them. For testers, the challenge is somewhat greater since they are expected to create test scenarios and test cases for features before they are even developed. They must understand user stories and conceptualize possible scenarios that the end-user is likely to face when using said features, and build test cases for that concept.

While developers have to write and put code through unit tests, QAs have to create automated test code, sometimes for parallel testing – all with the assistance of user stories rather than actually developed code.

Benefits of In-Sprint Automation

In-sprint test automation provides significant benefits that make it worth the time, effort, and resource investment. The most emphatic benefits are mentioned below, but one can harness many more if this approach to testing is applied effectively:

• Eliminates Automation Backlog: In-sprint test automation intends to complete all tests within a single sprint, almost in tandem with product development itself. Unlike traditional testing protocols that defer testing until after the product or feature delivery, in-sprint tests are built based on the user stories rather than waiting for the product code to come through before building the test code. Doing so prevents automation backlog since one can execute all tests simultaneously with software development.
• Facilitates Shift Left Testing: In-sprint test automation is a classic example of shift left testing, as it moves tests earlier in the development pipeline. It also automates tests, which adds to the efficacy, accuracy, increased coverage, and speed that the shift left approach seeks to achieve.

• Facilitates earlier time to market: By aligning tests in tandem with development, the team gets more done within the same time. The time required for testing is noticeably reduced, contributing to faster releases and a higher competitive edge.
• Promotes inter and intra-team collaboration: The fact that software quality should be a shared responsibility has been well-established by now. In-sprint automation testing requires individuals across teams – devs, testers, product managers, sales personnel, customer success experts, researchers, etc. – to work together. To extract the maximum benefits of in-sprint test automation, an organization must invest in creative, collaborative mindsets and mechanisms.

Primary Challenges in In-Sprint Test Automation (and how to overcome them)

If you’re considering the implementation of in-sprint automation testing, it is imperative that you and your team have end-to-end clarity on what challenges might show up in the process.

Here are a few that you will almost inevitably encounter:

Choosing the right automation tool/framework

If a team has already started with automation, they might have an easier time using the same tools to move tests in-sprint. It is possible, however, that the tools and test scripts will have to be recalibrated to match the expectations of speed and accuracy within the sprint.
However, if a team decides to move from a largely manual test setup to in-sprint automation, they will have to spend time researching and trying out tools to understand which one meets their technical, business and industry requirements. Building automation scripts requires a certain measure of expertise, especially when they must be shaped using users stories and not actually finished software. You must also spend time and effort training QAs to use a new tool.

Solution: In all honesty, it is best to hire an expert in this case. It would be prohibitively expensive to hire an entire team of automation experts for most businesses, so hiring a single person with requisite experience can be a compromise. Allow this person to supervise the selection of the right tool, creating a roadmap for adopting automation, and training existing QA personnel.

This will, of course, require an investment of time, money, and human resources. As with most automated testing, the initial increased expenditure will generate significant ROI in the long run by instituting faster and more reliable tests.

Inadequately Detailed User Stories

Testers have to build test scenarios and scripts from user stories, which product managers are responsible for shaping and delivering. However, the PM doesn’t always immediately have comprehensive details on each feature, including customer expectations and industry success standards. If they are not able to provide this data, testers cannot determine acceptance criteria for tests, which defeats the entire purpose of testing.

Solution: Start creating high-level scenarios that can test the feature’s efficacy in the most common real user conditions. Additionally, the shift left practice of including testers in product brainstorming sessions will also give them the context to create more nuanced tests that cover more scenarios.

Accessing a sufficient number of real browsers and devices

When test scripts are shaped off user stories, they can lack the specificity of tests created to match actual software code. In this scenario, verifying product behavior in real user conditions is more important than ever. Otherwise, test scripts just become a product of guesswork, and the results of such tests cannot be taken seriously.

Solution: Use a cloud-based test infra platform like BrowserStack that provides real browsers and devices. BrowserStack Automate, in particular, allows users to access 3500+ real browsers and devices for automated testing via Selenium, Cypress, Playwright, and Puppeteer. To further accelerate in-sprint testing, Automate allows parallel testing – running tests simultaneously on different browser-device-OS combinations.

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Estimating test time & dependencies

A user story does not always provide the details necessary from a tester’s point of view. It is quite common for testers to inaccurately estimate the complexity of the feature, and consequently the test. They can also miscalculate the time needed to complete the test, as well as its dependencies – test data, device models, browser versions and the like.

Wrong estimations, especially when repeated, slows down testing and can stretch tests beyond the timeline of a single sprint. This stops test automation from actually being “in-sprint”.

Solution: If a QA team finds itself constantly having to redesign code for performance, security, and validation issues, there is a fundamental gap in understanding between PMs and testers. To address this, QA managers might have to specify what user stories should specify, so they can make better estimations. Regular meetings to update QA teams on product-centric decisions can also keep them on the same page and give them a closer idea of what tests will require.

Inadequate Collaboration

It is imperative that every team must agree on product requirements and the software’s expected performance in production. Without such consensus, the QA team cannot finalize the acceptance criteria that would qualify tests as passed/failed.

Unfortunately, achieving such a consensus is far easier said than done. Creating seamless collaboration between teams requires a change in not just workflows, but how individuals approach the idea of development and testing itself. Across all teams, individuals must view software quality as a collective responsibility rather than just being QA’s job.

Without such consensus, QA teams will find themselves creating tests that don’t meet customer expectations as well as product requirements (business and technical). Additionally, they might have to deal with changing requirements (often last minute) from different teams. Needless to say, this will slow down tests, confuse and frustrate testing, and result in the delivery of a sub-par product.

Add to that the need to ensure that any new code does not disrupt the software’s existing functionality, and you’ll see how the whole process becomes a nightmare to manage, especially within narrow timelines.

Solution: This problem can only be meaningfully and sustainably solved with a change in culture. Management must take the time to convey the importance of collaborative processes to every team. The company’s reward structure might also need to be restructured to incentivize sustained cooperation and blend it into the everyday fabric of work.

Once again, include testers from the very beginning of product development so that they have complete context on how it is meant to perform. If QAs stay on top of all ideation and industry research (conveyed in team-wide sessions), they naturally gain the insight required to create tests even before product code is complete.

Upskilling and Training for Automation Engineers

Automation technologies evolve quickly, making it difficult for engineers to stay current. Without continuous training, teams may struggle to implement best practices or use new tools effectively, which can slow down automation progress.

Solution: Invest in ongoing learning through workshops, online courses, and peer mentoring. Foster a culture of knowledge sharing and experimentation to keep skills sharp and aligned with project needs.

Inefficient or Incomplete Reporting

Test reports that lack detail or clarity hinder the team’s ability to quickly understand test outcomes and identify critical issues. Inefficient reporting can cause delays in debugging and decision-making. When stakeholders cannot access meaningful insights from automation reports, confidence in testing diminishes.

Solution: Adopt reporting tools that provide comprehensive, easy-to-understand dashboards and automated notifications. Ensure reports are accessible to all stakeholders for transparency and timely action.

Dealing with False Positives and False Negatives

False positives (tests failing without real issues) and false negatives (tests passing despite defects) disrupt the testing process and waste valuable time. They erode trust in automation, causing teams to doubt test accuracy. Managing these inaccuracies is critical for reliable sprint automation.

Solution: Continuously refine test scripts for stability, use robust element selectors, and implement retry mechanisms. Employ detailed logging and root cause analysis to quickly identify and resolve inaccuracies.

Integrating Third-Party Tools Seamlessly

Automation ecosystems often involve multiple tools such as CI/CD platforms, test management software, and reporting systems. Poor integration between these tools creates manual overhead, data inconsistencies, and workflow interruptions, reducing automation efficiency.

Solution: Select tools with strong APIs and native integration capabilities. Automate data synchronization across platforms and standardize the toolchain to create smooth, end-to-end workflows.

Achieving Scalable Test Environments

As test suites grow, the demand for diverse devices, browsers, and environments increases, often outpacing available resources. Limited infrastructure can cause test backlogs, longer execution times, and reduced parallelism, affecting sprint velocity and coverage.

Solution: Leverage cloud-based test farms and containerized environments to dynamically scale resources. Utilize parallel execution to speed up testing and maximize resource usage.

Flaky UI Tests that Break Testing Cycles

UI tests are particularly prone to flakiness due to timing issues, dynamic content, and environmental variability. Intermittent test failures cause unreliable test results, disrupt CI pipelines, and undermine confidence in the testing process.

Solution: Implement explicit waits, use stable element locators, and isolate test dependencies. Regularly monitor flaky tests, quarantine problematic ones, and apply retries to improve reliability.

Maintaining Continuous Testing at Scale

Sustaining continuous automated testing in fast-paced agile environments is challenging as test suites expand and applications evolve. Frequent changes, environment inconsistencies, and growing test execution times threaten continuous feedback and delivery goals.

Solution: Design modular, maintainable tests prioritized by criticality. Integrate testing tightly with CI/CD pipelines for automatic execution and monitoring. Optimize test suites for speed and reliability to sustain continuous feedback.

Other Common Challenges with In-Sprint Test Automation

In addition to the above-mentioned challenges, many teams face organizational and process-related challenges during in-sprint test automation. Addressing these obstacles is key to achieving smooth, efficient automation within agile workflows.

• Poor Communication Between Teams: Misaligned expectations and a lack of clear information flow can cause delays and rework. Establishing regular cross-team meetings and open communication channels keeps everyone aligned and informed.
• Lack of Developer-Tester Collaboration: When developers and testers work in silos, automation coverage and feedback loops suffer. Encouraging joint planning sessions and paired programming fosters shared ownership and faster delivery.
• Resistance to Adopting Automation: Teams comfortable with manual testing may resist new automation practices due to fear or uncertainty. Providing clear benefits, training, and involving the team early helps build acceptance and ease the transition.
• Limited Time for Automation in Sprints: Tight sprint timelines often leave little room for creating and maintaining automated tests. Prioritizing critical features and allocating dedicated sprint capacity ensures consistent automation progress.
• Skill Gaps and Inadequate Training: Without sufficient automation skills, teams struggle to develop effective tests. Continuous learning opportunities and mentorship help close skill gaps and increase confidence.
• Changing or Unclear Requirements: Unstable or ambiguous requirements complicate writing stable automated tests. Collaborating closely with product owners and refining backlogs regularly reduces ambiguity and rework.
• Difficulty Prioritizing Automation Tasks: Scattered automation efforts can dilute impact and miss critical coverage. Defining clear automation goals and focusing on high-risk areas maximizes return on investment.
• Weak Stakeholder Support: Lack of leadership buy-in limits resources and focus on automation initiatives. Demonstrating value through metrics and quick wins helps secure ongoing stakeholder support.
• Balancing Automation with Manual Testing: Striking the right balance between automated and manual testing under deadlines is challenging. Setting clear guidelines for when to automate versus test manually optimizes coverage and resources.
• Low Visibility into Automation Progress: Without transparent progress tracking, issues may go unnoticed and confidence wanes. Using dashboards and regular updates improves visibility and encourages proactive problem-solving.

Best Practices to Incorporate in-sprint Automation into Your Process

Here are the best practices to incorporate in-sprint automation

Make QA a part of the Sprint Meeting

Include QAs in your in-sprint test automation process so as to plan the objectives effectively.

QAs know how long each specific feature and the ticket associated with it will take to be tested entirely, and you can accordingly assess the time needed for each task and prioritize them.

QAs would also know what features should be tested and how they will work. Therefore, they can help in creating test cases faster.

Avoid Sequential Test Automation

Sequential test automation may not be the right choice at a time that demands fast-paced release frequencies. Adopt a test method that can drive in-sprint automation efficiently instead of following a sequential approach every time. You can use the following methods for testing:

• Test-driven Development (TDD): In this approach, tests are created before the functionality. The test cases fail initially, as the functionality is not fully developed. However, the developers write code and test again to pass all the tests. Once the tests are successful, they move on to the next functionality, thus ensuring the facilitation of bug-free codes early on in the development process.
• Behavior-driven Development (BDD): It facilitates the creation of test cases that can be understood by all business partners with or without technical know-how. Here the Gherkin language is used to create test cases. This allows all stakeholders to understand the process and align the tests with the business goals.
• Shift-left testing: Here, the testing activities are started right from the design and requirement phase; i.e testing starts even before the development is complete.

Talk to an Expert

Testing in DoD

DoD, or the Definition of Done, describes exactly what it means when it is stated that something is completed. It helps the team know when a task is ’truly’ completed and stay on the same page.

Though testing is usually not included as a part of DoD, it is good to add automated testing in DoD to ensure a task isn’t marked as completed until it is tested. By doing so, the QA will not fall behind and all tasks will be tested well within each sprint.

Progress Steadily

It is recommended to not do all your in-sprint test automation in one go. Start gradually and make steady progress.

Start with easy tests and allot some resources to build up test automation.

In the next sprint, you can add more complex tasks for testing and repeat this with every sprint.

Do Not Wait Until the Feature Development is Complete

Testers can start performing tests on partially developed features with initial documentation. When testing begins early, you can find errors and issues early. The failed tests can be sent back to the developers, on which they can work until all the tests are passed. Waiting until the final phase of development can bring more sophistication, making it hard to find errors, which will eventually consume a lot of time and increase the overhead costs.

Conclusion

Implementing in-sprint test automation is, by no means, a simple task. But, as explained above, its benefits are worth the investment. The speed with which end-users expect flawless software to be delivered requires in-sprint test automation to be consistently executed and executed well.

To achieve such streamlining, it is important to understand the in-sprint automation challenges you have to overcome. This article outlines the most common challenges and lays out possible solutions to them. The solutions may differ based on the nuanced workings of specific teams and companies, but for the most part, they will serve their purpose of facilitating smarter, faster, and more dependable testing.

Make sure you verify your feature behavior in real-user conditions. Use BrowserStack to access a real-device cloud of 3500+ real device-browser-OS combinations.

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