Software testing costs include more than the testing team’s budget. Defects can consume repair labor, delay releases, create data losses, increase maintenance work, and impose costs on software users. Historical NIST estimates put the annual U.S. impact of inadequate software testing infrastructure at $59.5 billion, while a 2023 Katalon survey found that 79% of respondents reported increased cost savings after applying test automation. The figures below show how those costs arise and where savings were estimated.
Contents
- National cost of inadequate testing
- Defect incidence and direct error costs
- Testing and quality work across the lifecycle
- Firm size and transaction scale
- Savings from earlier detection
- Automation and cost-efficient testing
National cost of inadequate testing
NIST’s The Economic Impacts of Inadequate Infrastructure for Software Testing estimated the annual U.S. economic cost of inadequate software testing infrastructure at $59.5 billion in a 2001 estimate. The study estimated that feasible improvements to testing infrastructure could reduce that annual impact by $22.2 billion. These are nominal-dollar historical estimates, not 2026 prices or current-dollar forecasts.
The $59.5 billion estimate was about 0.6% of the study’s roughly $10 trillion GDP denominator. The feasible $22.2 billion reduction was about 0.2% of that same denominator. The estimate also separated the burden between the people who build software and the people who use it:
| Group | Estimated inadequate-testing cost | Share of estimate | Feasible reduction |
|---|---|---|---|
| Software developers | $21.2 billion | About 40% | $10.6 billion |
| Software users | $38.3 billion | About 60% | $11.7 billion |
| Total | $59.5 billion | 100% | $22.2 billion |
Source: NIST, The Economic Impacts of Inadequate Infrastructure for Software Testing, 2001 estimate.
The national calculation was an extrapolation based on transportation manufacturing and financial-services case studies. It is useful for showing the scale and distribution of the problem, but it should not be read as a current national spending measure. In financial services specifically, NIST estimated total inadequate-testing costs of $3,342.5 million and feasible reductions of $1,512.6 million.
Defect incidence and direct error costs
The NIST transportation sample illustrates how testing costs can appear after a product or system is already in use. In the 2000 reporting year, 61% of transportation firms reported major software errors, and firms reporting them averaged 39.7 major errors per year. At the same time, 39% of firms reported no major software errors in the prior year, so the sample was not uniform across companies.
Minor errors were also common. Seventy-eight percent of transportation firms reported minor errors, and firms reporting them averaged 70.2 minor errors per year. A major transportation software error required an average of 268.4 labor hours to repair. Respondents also associated a software error with an average lost-data value of $604,900 per error and reported an average 1.5-month delay in introducing a new service associated with software errors.
The sample’s reported minor-error cost averaged $4,018,588 per affected firm-year. This is a cost measure for the NIST sample and reporting period, not a universal per-company price for software defects.
Financial-services users showed a different repair profile. In that NIST sample, 61% of firms reported major software errors, and affected firms averaged 40 major errors per year. Among firms reporting minor errors, the average was 49.4 minor errors per year, with 71% of firms reporting minor errors. A financial-services major software error required an average of 18.4 labor hours to repair. These financial-services results describe banks, savings institutions, and credit unions using FEDI and clearinghouse systems.
The contrast between 268.4 average repair hours for a transportation major error and 18.4 hours for a financial-services major error does not establish that one industry’s software is safer. It reflects the different samples, systems, error definitions, and operating conditions reported in the NIST study.
Testing and quality work across the lifecycle
Quality-related work begins before installation. Transportation companies in the NIST sample spent an average of 4.9 months and 1,399 staff hours researching software packages before purchase in the 2000 reporting year. The average reported cost of that purchase research was about $511,908 per company.
Better information was associated with potential savings in the purchase-search stage. Among transportation respondents with better-quality information, 58% said purchase-search costs could be reduced. Those respondents estimated savings of about 1.5 months and 582 staff hours. These were respondent estimates of possible savings, not measured savings from a controlled intervention.
Installation and acceptance testing also required substantial effort. Transportation installation and acceptance testing averaged about 564 in-house staff hours per installation, plus an average of $8,574 in external consulting services per installation. Respondents reported that about 25% of transportation installation costs were attributable to software errors.
Maintenance provides another route by which defects become recurring costs. Transportation annual software-maintenance expenditures ranged from $1,250 to $2.6 million per company. Respondents estimated that eliminating software errors could reduce annual maintenance costs by 14.4%, or about $10,905 per year. Because the expenditure range is wide, the $10,905 figure should be treated as the study’s reported average estimate rather than a percentage-based forecast for every company.
Redundancy can also be part of the cost response. About half of the transportation companies maintained redundant backup systems for an average of 5.6 months at $3,972 per month. The NIST figures connect these expenses with the operational consequences of software problems, but they do not provide a modern benchmark for cloud, backup, or managed-service pricing.
Firm size and transaction scale
NIST’s 2000 reporting-year estimates show that software-bug cost per employee could be much higher for smaller firms in the automotive and aerospace examples. The study estimated total software-bug cost at $1,466.1 per employee for small automotive firms and $277.8 for large automotive firms. For aerospace firms, the corresponding estimates were $2,141.4 per employee for small firms and $128.9 for large firms.
| Example | Small-firm estimate per employee | Large-firm estimate per employee | Reported leading component |
|---|---|---|---|
| Automotive | $1,466.1 | $277.8 | Major errors in small firms: $1,280.8 per employee |
| Aerospace | $2,141.4 | $128.9 | Installation-related bugs in small firms: $1,442.3 per employee |
The component estimates help explain the totals. For small automotive firms, major errors contributed $1,280.8 per employee to estimated annual bug cost. For large automotive firms, minor errors contributed $121.0 per employee. For small aerospace firms, installation-related bugs contributed $1,442.3 per employee, while major errors contributed $85.1 per employee for large aerospace firms.
The study also supplied hypothetical company examples. A 100-employee automotive company incurred estimated annual software-bug costs of $146,614, compared with $2,777,868 for a hypothetical 10,000-employee automotive company. In aerospace, the comparable estimates were $214,138 for a 100-employee company and $1,289,167 for a 10,000-employee company. The larger companies had higher total costs in these examples, even though their estimated cost per employee was lower.
Financial institutions were measured on a transaction-scale basis as well. For every $1 million in deposits and loans, NIST estimated bug costs of $98.20 for banks and savings institutions and $318.11 for credit unions. These ratios come from the 2000 reporting year and should not be treated as current-dollar benchmarks.
Savings from earlier detection
The timing of defect discovery can change the cost of correction. NIST’s Software Error Analysis reproduced a cited study finding that correcting a defect late in the lifecycle may cost more than 100 times detecting and correcting it when the defect is introduced. This is cited study evidence reproduced in NIST guidance, not a new NIST experiment.
The NIST national estimate assigned $10.6 billion of feasible testing-infrastructure cost reduction to software developers and $11.7 billion to software users. In financial services, feasible improvements were estimated to reduce router-and-switch developer costs by $975.0 million and FEDI-and-clearinghouse developer costs by $225.4 million. On the user side, estimated feasible reductions were $244.0 million for banks and savings institutions and $68.1 million for credit unions.
Transportation respondents also considered a defect-reduction scenario. They estimated that a 50% reduction in software bugs would reduce major-failure costs by 33% and minor-failure costs by 33%. These are scenario estimates from the 2000 reporting year, not observed savings from a later program.
The practical implication is that a testing budget should be compared with the full cost pathway: discovery, repair labor, lost data, delays, maintenance, backups, and user-side disruption. The available figures do not establish one universal testing percentage of development cost, but they do show why late defect costs can dominate the cost of finding defects earlier.
Automation and cost-efficient testing
NIST reports that combinatorial-testing studies conducted from 1999 to 2004 achieved fault detection comparable to exhaustive testing with a 20-fold to 700-fold reduction in test-set size. The NIST page was updated in 2025, but the study period for these results is 1999–2004. The result is a test-set-size comparison, not a promise of the same labor or tooling reduction in every organization. Source: NIST, Combinatorial Testing.
More recent survey data provides signals about automation practices rather than economy-wide costs. In Katalon’s 2023 State of Software Quality Report, 79% of respondents reported increased cost savings after applying test automation. The report also measured AI-augmented automation capabilities among respondents:
- Automated test-data generation: 34%.
- Test optimization and prioritization: 27%.
- Automated defect detection: 25%.
- Automated defect correction: 24%.
- Test planning and scheduling: 20%.
- Intelligent test management: 19%.
These percentages are survey results from Katalon’s 2023 report, not population-wide spending estimates or independent measures of return on investment. Source: Katalon, State of Software Quality Report 2023.
Taken together, the evidence points to several distinct cost levers: reducing the number of test cases needed for comparable fault detection, automating repetitive work, prioritizing tests, and finding defects earlier. The NIST economic-impact figures remain historical U.S. estimates based on case studies, while the Katalon figures describe 2023 survey responses. Keeping those periods, geographies, definitions, and evidence types separate is essential when using software testing cost statistics for planning.