Modern quality assurance rarely relies on a single testing approach. Manual testing still plays a critical role in exploratory work, usability checks, and business validation, while automated testing delivers the speed, repeatability, and consistency needed to support modern delivery practices.
The problem is that these activities are often managed in separate places. Automated tests may run by the quality team or as part of a CI/CD pipeline in the DevSecOps team, while manual testing, business validation, usability checks and requirements can be managed elsewhere. Each activity creates useful information, but when that information is split across tools, it becomes harder to understand the true state of quality.
The aim is to bring all of this quality information into one place, giving teams a clearer view of quality, testing progress, requirement coverage, and overall quality across the QA lifecycle.
Additionally, you need also keep in mind that with a proper DevSecOps practice testing can no longer standalone but needs to be fully integrated with the wider DevSecOps practice adhering to the same standards for code development, review, storage and security checks as any other piece of code would go through. With AI security threats now taking less than 10 hours to materialise from notification all code (automated test code included) becomes an attach surface to which the same security standards need apply.
Determining which tests should be automated (and which shouldn’t)
Before embarking on the automation journey it is critical that you understand your breakeven point. Typically, this would be done as part of your test strategy and take in factors like stability, time-to-market, resource availability and resource cost.
The following allows a quick way to figure this out.
To determine the exact number of times you need to run a test to achieve a positive return on investment (ROI), you must calculate its break-even point.
The Break-Even Formula
Use this formula to find the exact number of manual test runs (R) required to recover your automation investment:
R=(Ca+Cm)/ (Tm-Ta)
Where:
Ca = Cost to build the automation script (hours × hourly rate).
Cm = Cost to maintain the script over its lifetime.
Tm = Cost of running the test manually one time.
Ta = Cost of running the automated test one time. 6 Things to Consider When Calculating ROI for Intelligent Automation Projects | Blueprint
Here’s an example that may help
Imagine you have a test with the following estimated costs and times:
Development (Ca): 8 hours to write × $50/hour = $400
Maintenance (Cm): 2 hours of updates over time × $50/hour = $100
Manual Execution (Tm): 1 hour to run manually × $40/hour = $40
Automated Execution (Ta): 0.1 hours to run/review × $40/hour = $4
Let’s calculate the break-even runs:
R=(400+100)/(40-4)
R=500/36
R=13.89 times
In this scenario, you need to run the test at least 14 times to see a positive ROI.
Key Factors That Change the Number
Test Stability: Frequently changing apps increase maintenance costs (Cm), which will in turn increase the number of required runs required to break even.
Execution Frequency: Tests run multiple times a day (like in CI/CD) hit positive ROI much faster.
Time-to-Market: Automation executes faster, saving project time and have additional benefits that standard ROI formulas often miss.
Once you have determined the number of runs required and whether it is worth automating your tests you’ll need to be careful to not create data silos for your tests and test results.
Why Separate Testing Tools Create Silos
When manual and automated testing are handled in different systems, teams can find themselves working with only part of the quality picture. Results are spread across multiple tools, requirement coverage can be difficult to assess across all test types, and reporting often requires information to be manually brought together. All this adds processing time and cost (and maybe confusion) to your quality reporting
In practice, a team might know that automated tests have passed and that manual testing is progressing well but still struggle to answer a simple question: Are we truly covered, and what does quality look like right now?
In reality there are so many types of testing that no single automated tool can cover them all. e,g, load, performance, API, GUI, etc. The trick is not to try and find a tool that can do it all but instead look for a platform that has strong integration capabilities across the different quality areas. This way you and your team can use the appropriate tool for each specific type of testing while bring the outputs together for that single quality metric.
Bringing testing activities together provides teams with greater visibility and a clearer understanding of overall quality.
Bringing Manual and Automated Testing Together
Your Quality System should provide a central place to view, manage, and report on both manual and automated testing activities.
Automated tests can continue to be executed using a team’s preferred testing framework and CI/CD process. Automated test results can be automatically pushed into your Quality Management System and viewed alongside manual test executions.
Rather than reviewing automated results in one tool and manual testing progress in another, teams gain a consolidated view of their testing activities.
Your Quality Management System should not replace automated testing frameworks or CI/CD tools. Instead, it brings automated test results into the wider QA lifecycle, where they can be viewed and reported on alongside manual testing providing a holistic view of quality end-to-end and allow teams to make clear decisions easily.
Connecting Testing to Requirements and Coverage
Knowing that tests have passed is useful, but it is only part of the story. Teams also need to understand what has been tested and whether requirements are covered.
Requirements should be linked to both manual and automated tests. This creates traceability between what needs to be delivered and the testing used to validate it, regardless of whether a test is executed manually or automatically.
When requirements, manual tests, and automated tests are connected in one place, teams can see where test coverage exists and where gaps may remain.
Enterprise Tester’s Test Query Language (TQL) or Frankly’s advanced filtering capabilities can be used to query requirements based on test coverage, helping teams identify requirements with associated tests and areas that may require additional testing.
Because coverage includes both manual and automated testing, teams gain a more complete view of validation across the project rather than two separate pictures that need to be reconciled later.
Increasing Test Coverage with Variable-Driven Data
Creating individual test scripts for multiple variations of test data can be time-consuming. Enterprise Tester’s Compiler Suite enables teams to define variables within test procedures and use sets of test data to generate multiple test scenarios.
Different combination methods can be used to generate test scenarios based on the required level of coverage, including row-by-row, minimum test cases, and pairwise coverage. Teams can review the generated scenarios and select the tests relevant to their testing needs.
Compiler Suite data can also be exported for use by automated tests. This allows variable-driven data created within the QMS that supports both manual and automated testing activities and saves a bucket load of time if you were to manually create all data combinations.
By using the same variable-driven approach across testing activities, teams can increase test coverage while reducing the time required to manually create and maintain multiple test variations.
Better Reporting and a Clearer View of Quality
Bringing manual and automated testing together makes reporting more meaningful. Instead of reporting on individual streams of testing in isolation, Your QMS should provide visibility across the complete testing effort.
QA teams and test leads can monitor progress across manual and automated testing, report on requirement coverage, and better understand the overall state of quality.
For stakeholders, this provides a clearer view of testing progress and release readiness without needing to interpret results from multiple testing tools.
A Complete View of Quality
Manual and automated testing both play an important role in a successful QA practice. They serve different purposes and provide different insights into quality.
The challenge comes when those activities are separated across tools and processes.
Enterprise Tester helps break down these silos by bringing manual testing, automated test results, requirement traceability, test coverage, and reporting together in one place.
With a consolidated view of testing across the QA lifecycle, teams can better understand what has been tested, identify gaps in coverage, and make more informed decisions about overall quality and release readiness.