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Return on investment, or ROI, is a performance measure used to evaluate the results of an investment. In test automation, calculating ROI allows you to quantify the value of your automation efforts in relation to their costs. This metric provides critical insight that can guide your testing strategy.
ROI gives you a concrete number you can use to justify automation expenditures and compare the profitability of manual versus automated testing. Rather than relying on hunches or intuition, you have hard data to present to stakeholders when requesting automation budgets. The ROI figure also helps you optimize the testing approach over time by revealing which test types and systems provide the biggest bang for the buck.
Calculating ROI forces you to closely analyze the costs and benefits of test automation. This exercise invariably surfaces automation insights you may otherwise miss. For example, a detailed cost analysis could show opportunities to improve efficiency in your automation framework. Or a closer examination of quality improvements may reveal weak spots in your manual processes.
In addition, the ROI assessment requires you to make projections about efficiencies gained and expenses reduced through automation. Making these forecasts prompts critical thinking about potential automation outcomes. The resulting insights better equip you to maximize the return on your automation investment.
The ROI calculation also facilitates apples-to-apples comparisons between manual and automated testing costs. By standardizing divergent metrics into a uniform ROI formula, you can clearly demonstrate the monetary value added by automation. This analysis highlights automation"s cost advantages in a way that resonates with business stakeholders concerned with the bottom line.
A primary driver of test automation ROI is the cost differential between manual and automated testing. When calculating your ROI, you must closely analyze and compare the expenses associated with each approach. This allows you to accurately project how automation will reduce your overall testing costs.
Manual testing requires extensive human effort. Testers must develop and execute test cases, document results, re-test fixes, and conduct regressions. The more tests you run, the higher your labor costs climb. Automated testing also incurs some labor costs for framework development and test maintenance. However, executed tests have near-zero incremental costs. The same set of automated checks can be run repeatedly without incurring additional overhead.
Accounting for labor savings is not always straightforward. Be sure to consider both direct test execution costs as well as ancillary expenses like result analysis and reporting. Factor in the fully-loaded hourly rates for testers and related team members based on salaries, benefits, and overhead. Also weigh the costs of recruiting, hiring, training, and attrition for manual testing staff. Tools like timed recordings and effort tracking systems can provide data to quantify labor needs.
When comparing costs, calculate averages based on typical test execution parameters for your environment. For example, determine metrics like:
- Hours to manually execute the full regression suite
- Time for automated suite execution
- Number of test runs per release cycle
- Effort to analyze and document test results
Keep in mind that manual execution effort grows exponentially as the scope of testing expands. Compare how manual versus automated costs scale when you increase test cycles, integrate more systems, lengthen regression suites or accelerate release velocity. This illustrates how automation provides compounding cost savings over time, especially as testing needs grow.
A primary source of cost savings from test automation is reduced labor needs for test execution. However, accurately projecting and accounting for these labor savings requires careful analysis. Failing to thoroughly account for labor reductions can cause you to underestimate the ROI of test automation.
Start by calculating your current manual testing costs. Add up the fully-burdened hourly rates for all testers, SMEs, and other staff participating in test activities. Factor in salaries, benefits, overhead, and allocating shares of costs like test environments. Multiply the blended hourly rate by the total hours invested in executing test cases, analyzing/reporting results, bug fixing cycles, and re-testing during each release. This quantifies your current manual testing expense.
Next, estimate the labor hours needed to develop and maintain your test automation suite. Consider time for activities like: initial framework development, test scripting, reviewing/updating tests, investigating test failures, and maintaining frameworks. Don't overlook ancillary costs like tool licenses, infrastructure, and outside contractors.
Compare your projected automation costs to current expenses to reveal labor savings. For example, if manual testing costs $100,000 per release, and automation will cost $30,000, your estimated labor savings is $70,000 per release cycle.
It's essential to carefully validate your labor savings assumptions during test automation pilots. Time automation and manual testing efforts using a representative sample of test cases over multiple iterations. Compare the results to refine projections, reveal invalid assumptions, and build confidence in the numbers.
Re-validate estimates periodically as processes evolve. As developers improve at creating automatable code, time savings may increase. Or changes in test practices or systems could alter cost/benefit expectations. Regularly update estimates to reflect the latest real-world data.
Anticipate that time savings will expand as you scale test automation. Adding test cases increases execution time for manual testing but has minimal impact on automated checks. Thus, labor savings grow exponentially as your automation coverage expands.
Also account for ancillary time savings like faster result analysis and regression setup. Automated reporting reduces log review time. Tests can be scheduled and deployed automatically versus manually configuring test data.
Reducing the cycle time of your testing processes can significantly impact ROI projections for test automation. Slow and inefficient manual testing cycles reduce release velocity, delaying value delivery and revenue realization. Test automation not only lowers execution costs, but it speeds up testing cycles to accelerate releases. When calculating ROI, factoring in testing cycle time reductions reveals the total economics of automation.
Cycle time savings start with test execution. Manual testing can take days or weeks to complete full regression suites between releases. Automated checks execute tests in just hours or minutes, freeing up human testers for higher-value work. Include these test execution time savings when projecting ROI.
However, don"t overlook delays between test execution and release. Manual testing often bogs down this critical path due to activities like:
- Testers manually documenting and reporting issues found during test runs. Consolidating logs, screenshots and steps to reproduce takes time. - Triaging defects with developers and product managers to prioritize fixes. Ping-ponging issues can lead to delays.
- Waiting for fixes and re-testing them. Several iterations may be needed to confirm resolution.
Test automation accelerates these processes since tooling automatically logs and reports test failures, reducing the need for manual documentation. Automated re-testing confirms fixes in hours without wasting tester time. Defect data can automatically sync with tracking systems, saving triaging overhead.
A Fortune 500 financial services company cut their full testing cycle from 21 days to just 5 days after implementing test automation. This enabled them to release high-quality updates more frequently, getting new features to market faster.
Leading test teams measure cycle time reductions from automating specific test types to quantify time savings. Unit test automation may shorten development feedback loops from days to hours. Automating integration testing could collapse the time between code complete and functional verification. Reducing user acceptance testing cycles accelerates final release approvals.
Analyze your current manual processes to identity cycle delays and bottlenecks. Estimate where test automation could trim waste and accelerate steps. Factor those projected time savings into your overall ROI forecast. Like lowered execution costs, reducing cycle times boosts ROI by both decreasing expenses and getting releases to market faster.
Improving software quality takes time, effort and expertise, which all translate into real costs. However, the monetary impact of quality issues is just as real, if not more so. When calculating automation ROI, factoring in quality improvement costs and benefits provides critical context.
Direct Costs - Resources spent identifying, reproducing, fixing and redeploying fixes for defects represents tangible dollars out the door. For complex issues, these costs can run into the thousands per incident.
Customer Experience Impacts - Bugs that impact user workflows or cause crashes can reduce customer satisfaction. Even small quality issues can cause losses by damaging your brand reputation and loyalty.
Test automation fills quality gaps missed by manual testing. A leading IT services firm found that automated integration testing improved defect detection rates from just 37% to over 95%, preventing costly bugs from reaching production.
One major healthcare technology company spent approximately $3 million over two years to build out an automated testing practice. But after reducing production defects by 62%, they calculated over $7 million in direct incident savings alone, for a total ROI of 134%.
Strike a realistic balance between quality and cost in ROI projections. Perfection is not feasible or necessary. The goal is optimizing the level of quality your customers demand at a cost your business can afford. This "just right" balance maximizes ROI.
Beyond just test execution labor savings, take a holistic view of efficiency gains across your end-to-end testing process when projecting automation ROI. Manual testing is often plagued by waste and inconsistency that automation can minimize. Analyzing current processes reveals opportunities to optimize beyond just test runs.
Requirements validation is an area primed for efficiency improvements. Manual review of requirements is time-consuming and prone to oversights. Automated validations using AI-based tools can analyze requirements documents and specifications to instantly identify ambiguities, gaps, conflicts, missing information, and deviations. This prevents defects originating in requirements from tainting downstream work.
Test data bottlenecks also delay testing cycles. Manually configuring test environments and prepping data is complex and repetitive. Test automation frameworks can generate data on demand, spin up environments dynamically, and tear them down after checks complete. Efficient data pipelines prevent test asset shortages from throttling execution velocity.
Automated reporting and analytics provide a wealth of efficiency opportunities compared to manual processes. Tests automatically log runtime details and generate consolidated reports showing statuses, failures, screenshots, logs, videos and more. This eliminates tester time wasted documenting results. Real-time dashboards reveal environment health and test coverage. Analytics identify frequently failing test cases or unstable systems needing attention.
Human inspection of test results is inefficient compared to automated analysis. AI engines can instantly process test logs to detect failures, group related defects and determine root causes. This reduces triage time compared to humans parsing volumes of manual test records. Prioritization based on code relationships and past failures is more consistent than manual intuition.
Eliminating redundant and obsolete test cases also boosts efficiency. Test maintenance often takes a backseat as teams scramble to meet deadlines. Automated test impact analysis can identify redundant and outdated checks based on code changes. This prevents wasting execution time and maintenance costs on low-value tests.
When calculating test automation ROI, one factor that can dramatically impact projections is accounting for ongoing maintenance costs. While automation reduces test execution labor, maintaining automated checks requires dedicated time from staff. Failing to budget for test maintenance expenses can undermine the ROI realized from automation over time.
Like any software application, test automation suites require care and feeding. As the application under test evolves, automated checks need to be updated to avoid failures and maximize value. Effort is required to investigate test failures, fix broken tests, and realign checks with changes in the AUT or test data.
Over time, test maintenance costs add up. Leading teams estimate 10-20% of total automation engineering time goes towards test maintenance. For a large suite this can equate to multiple full-time resources. Withoutbudgeting for maintenance, test teams often struggle to balance new test development with upkeep of existing scripts. Lack of maintenance causes test rot sets in, increasing failures and decreasing ROI.
The volume of test maintenance work depends on several factors. Frequent code changes in the application require more test adjustments. Complex applications with many integrated dependencies typically see higher failure rates. The framework design and stability also impacts maintenance needs. Brittle tests using lots of locators or custom code increase rework.
Test teams finding themselves stuck in an endless maintenance cycle should investigate root causes. Refactoring tests to remove selectors and leverage object maps usually improves maintainability. Reviewing application stability and test practices helps minimize test churn. Improving testability of the AUT simplifies test updates.
Maintenance costs vary by test type. Functional UI tests typically require the most care due to frequent UI changes. Lower-level API or unit tests are less fragile. Consider maintenance needs when allocating test budgets across different test types.
New teams should take heart that maintenance costs decrease over time. As processes mature and systems stabilize, less rework is needed. However, some ongoing costs remain so teams should budget appropriately. Strike a sustainable balance between new test development and maintenance of business critical test assets.
Getting the most ROI from test automation requires taking a long view. While initial automation costs may exceed manual testing expenses, the benefits compound over time. Teams need a multi-pronged strategy to maximize their return on automation investments.
Many test automation mistakes stem from focusing too much on short-term metrics versus long-term efficiency and quality gains. For example, some teams fall into the trap of over-automating during the first phase. They script an unsustainably large portion of tests before processes and frameworks have matured. This inflates initial costs and maintenance burdens before automation can demonstrate its full benefits. A wiser approach is to automate incrementally, targeting repetitive and high-risk tests first. As processes improve, more complex test cases can be added.
Successful teams also review automation results periodically to reveal optimization opportunities. For example, analytics may show certain test types or application modules have disproportionately high failure rates. These problem areas can be targeted for fixes or framework improvements to boost ROI. Reports also help identify redundant or obsolete tests consuming resources without adding value.
Adopting the latest tools and techniques keeps automation frameworks up-to-date. For example, shifting from linear scripts to keyword-driven frameworks improves maintainability. AI-based self-healing reduces test maintenance overhead. Test impact analysis identifies critical test cases versus those made redundant by code changes. Introducing these innovations over time future-proofs test assets.
Expanding test coverage and types is another avenue for optimizing ROI. Once a strong foundation is built, automation can expand into areas like security, performance, microservices and database testing. This spreads benefits across more of the delivery pipeline.
Leading teams also review processes periodically to remove waste that saps automation productivity. Removing bottlenecks around test environments, data and tool access makes frameworks more efficient. Optimizing how failures are triaged and fixed accelerates resolution.