Proving the return on investment (ROI) for testing tools can be challenging but is crucial for justifying their adoption. Here are some strategies and considerations that can help demonstrate the ROI for testing tools:
Efficiency Gains:
Measure the time saved by using automated testing tools compared to manual testing. Automated tests can execute quickly and consistently, leading to faster feedback on software quality.
Calculate the reduction in testing time, especially for repetitive tasks, and demonstrate how this contributes to faster release cycles.
Example: 100 test cases
Manual Testing = 100 * 15mins = 1500mins ( 3.1 person days)
Automated Testing = 100 * 5mins (50 parallel) = 10mins (10 mins)
Other things to consider when calculating for Automation vs Manual
Automation
Manual Testing
Bug Detection and Prevention:
Track the number of defects identified through testing tools both pre and post-production. Reducing the number of bugs in production can lead to cost savings associated with fixing and addressing issues later in the development lifecycle.
Estimate the potential cost of bugs that could have been missed without using testing tools and compare it with the investment made in those tools.
Cost of 1 bug in prod = Choices (10k USD, 20k USD, 30k USD, 50k USD, 100k USD, 500k USD)
Cost of DigyDashboard =Smaller amount per year on Enterprise Licence subject to condition and various other parameters compared to the production loss
Comparing this, the cost of investment can be recouped just by saving a few defects.
Resource Utilisation:
Evaluate the utilisation of testing resources. Automated testing tools can run tests 24/7, allowing for better resource allocation and utilization.
Compare the workload and productivity of testing teams before and after implementing testing tools.
Resource required for creating various QE reports (before DigyDashboard) = > 1
Resource required for creating various QE reports (after DigyDashboard) = 0
Resource required for analysing test failure (before DigyDashboard) = 5
Resource required for analysing test failure (after DigyDashboard) = 2
Scalability:
Demonstrate how testing tools facilitate scalability by efficiently handling an increasing number of test cases and configurations.
Show how the testing process can adapt to changes in project size and complexity without a proportional increase in resources.
Example 1:
Resource required for creating QE reports for 10 projects (before DigyDashboard) = 5
Resource required for creating QE reports for 10 projects (after DigyDashboard) = 1
Resource required for creating QE reports for 30 projects (after DigyDashboard) = 1
Example 2:
Automated execution of 100 tests (serial) = 100 * 3mins = 300 mins
Automated execution of 100 tests (parallel) = time taken by the slowest test (~3mins)
Release Confidence:
Measure the improvement in overall software quality and the confidence in releasing new features or updates to production.
Consider the impact on customer satisfaction and brand reputation due to the delivery of higher quality software.
Cost Savings:
The chart above shows how the cost of fixes increases as the defects are found late in the SDLC- Source NIST (National Institute of Standards and Technology)
Compare the costs of implementing testing tools against potential savings in terms of reduced testing time, decreased defect fixing costs, and improved overall development efficiency.
Quantify the financial benefits of reduced manual testing efforts, especially for large and complex projects.
Reduced defect fixing cost:
| Cost of fixing defect | Number of defects | In Development | In Test Environment | In Production |
| £100 | 100 | $10000 | $30000 | $300000 |
| % of issues caught in development | Cost Saving | Development | Test Environment | Production |
| $10000 | $30000 | $300000 | ||
| 10% (10) | 29,000 | |||
| 20% (20) | 58,000 | |||
| 50% (50) | 145,000 |
Compliance and Security:
If applicable, demonstrate how testing tools contribute to meeting regulatory compliance and security standards, avoiding potential legal or financial consequences.
The Accessible Canada Act Deadline: Failure to comply could result in a fine of up to $250,000.
Accessibility Compliance tool : 4-8% of the fine amount
Feedback Loops:
Highlight the faster feedback loops provided by testing tools, which enable developers to identify and fix issues early in the development process, reducing the overall cost of fixing defects.
Average developer waiting time for feedback (sequential): 3 – 10 hours
Average developer waiting time for feedback (parallel) : <30mins
Training and Skill Development:
Factor in the cost and time savings associated with training testers and developers to use testing tools effectively.
Ultimately, creating a comprehensive business case that combines these factors and presents a clear before-and-after picture will help in making a compelling argument for the ROI of testing tools. It’s important to tailor the metrics and measures to the specific context and goals of your organization.
ROI = ( Savings from Efficiency +
Savings from Defect Prevention +
Cost Savings +
Scalability Benefits +
Release Confidence +
Compliance Benefits –
Initial Investment
) / Initial Investment
Where:
Please note that the values for each component should be quantifiable and specific to your organization. The formula is a starting point, and you may need to customize it based on the specific metrics and parameters relevant to your context.
Keep in mind that ROI is not always solely about monetary gains; it can also involve improvements in quality, customer satisfaction, and overall project success. Therefore, consider both quantitative and qualitative factors when assessing the impact of testing.