The Impact of Test Coverage

Several factors can lead to less than 100% test coverage with manual testing teams:

1. Time constraints: Testing projects often have tight deadlines, and manual testing can be time-consuming. Due to these time constraints, testing teams may not be able to test all possible scenarios and combinations within the given timeframe, leading to lower test coverage.
2. Resource limitations: Manual testing requires human effort, and there may be a limited number of testers available for a project. Insufficient resources can result in incomplete test coverage, as some test cases might not be executed due to a lack of available testers.
3. Test prioritization: Test teams often prioritize test cases based on their risk, complexity, and impact on the application’s functionality. As a result, some lower-priority test cases may not be executed, leading to less than 100% test coverage.
4. Human error: Manual testing is prone to human error, which can result in missed or improperly executed test cases. Testers might overlook certain scenarios or misunderstand the requirements, leading to gaps in test coverage.
5. Complex application scenarios: Some applications have complex business logic, numerous features, or multiple user paths, making it difficult for manual testing teams to achieve 100% test coverage. In these cases, covering all possible scenarios may be impractical or even impossible.
6. Incomplete or ambiguous requirements: If the requirements for an application are not clearly defined, testers may have difficulty determining what needs to be tested and how to create test cases that cover all aspects of the application. This can result in gaps in test coverage.
7. Test data challenges: Proper test data is essential for effective testing, but creating and maintaining realistic test data can be time-consuming and challenging. Manual testing teams may struggle to cover all test cases if they don’t have access to suitable test data.

While it’s desirable to achieve 100% test coverage, in practice, it’s often not feasible, especially with manual testing. Teams should focus on prioritizing high-risk areas and critical functionality to ensure that the most important aspects of the application are tested thoroughly. Test automation can also help improve test coverage by speeding up test execution and reducing human error.

Calculating Test Coverage

Calculating the expected test coverage in a manual testing scenario depends on several factors, including the number of requirements, available resources, and testing time constraints. Here’s a step-by-step process to estimate test coverage:

1. Analyze the requirements: Review the set of requirements to understand the complexity of the application and identify the critical functionality that must be tested. Break down the requirements into individual test cases or scenarios.
2. Estimate the number of test cases: Based on the analysis, estimate the total number of test cases required to cover all requirements. Keep in mind that some requirements might have multiple test cases, while others might have only one.
3. Assess available resources: Determine the number of testers available for the project and their skill sets. Consider the total testing effort, such as the number of hours each tester can work on the project per day or week.
4. Estimate testing effort per test case: Calculate the average time it takes to execute a single test case manually, including test preparation, execution, and reporting. This may vary depending on the complexity of the test cases and the expertise of the testers.
5. Calculate total testing capacity: Multiply the number of testers by the hours they can dedicate to testing during the project’s duration to find the total testing capacity.
6. Determine the number of test cases that can be executed: Divide the total testing capacity by the estimated testing effort per test case to determine how many test cases can be executed within the given constraints.
7. Calculate test coverage: Divide the number of test cases that can be executed by the total number of test cases and multiply by 100 to obtain the test coverage percentage.

Please note that this calculation is based on estimations and assumptions, and the actual test coverage may vary depending on factors like tester efficiency, test case complexity, and the accuracy of the initial estimations. To maximize test coverage within the given constraints, prioritize test cases based on risk, impact on the application’s functionality, and the likelihood of defects.

A simple scenario

Let’s assume the following scenario:

1. Analyze the requirements: You now have 500 requirements for the application.
2. Estimate the number of test cases: Assuming the same ratio of test cases per requirement as before, you would now have 1,000 test cases (500 requirements * 2 test cases per requirement) to cover all requirements.
3. Assess available resources: The number of testers and their available time remains the same. You have a team of 4 manual testers, and each tester can work 6 hours per day on testing activities.
4. Estimate testing effort per test case: On average, it takes 30 minutes (0.5 hours) to prepare, execute, and report a single test case manually.
5. Calculate total testing capacity: The 4 testers can work a combined 24 hours per day on testing (4 testers * 6 hours/day). Assuming a 5-day workweek and a 4-week project duration, the total testing capacity is 480 hours (24 hours/day * 5 days/week * 4 weeks).
6. Determine the number of test cases that can be executed: With 480 hours of total testing capacity and an average testing effort of 0.5 hours per test case, the team can execute 960 test cases (480 hours ÷ 0.5 hours/test case). However, in this scenario, there are 1,000 test cases, so the team will not be able to execute all test cases within the given time frame.
7. Calculate test coverage: In this scenario, the team can execute 960 test cases out of 1,000 total test cases, so the test coverage is 96% (960 executed test cases ÷ 1,000 total test cases * 100).

Please note that these calculations are based on hypothetical assumptions and should be tailored to your specific project context. Factors such as tester efficiency, test case complexity, and the accuracy of initial estimations can influence the actual test coverage. Prioritize test cases based on risk, impact on the application’s functionality, and the likelihood of defects to maximize test coverage within the given constraints.

Not so fast…

Often, you won’t have the luxury of time. Let’s recalculate the test coverage with 500 requirements and a 2-week project duration while keeping all other factors the same:

1. Analyze the requirements: You have 500 requirements for the application.
2. Estimate the number of test cases: Assuming the same ratio of test cases per requirement as before, you would now have 1,000 test cases (500 requirements * 2 test cases per requirement) to cover all requirements.
3. Assess available resources: The number of testers and their available time remains the same. You have a team of 4 manual testers, and each tester can work 6 hours per day on testing activities.
4. Estimate testing effort per test case: The average time to prepare, execute, and report a single test case manually remains 30 minutes (0.5 hours).
5. Calculate total testing capacity: With the 2-week project duration, the total testing capacity is now 240 hours (24 hours/day * 5 days/week * 2 weeks).
6. Determine the number of test cases that can be executed: With 240 hours of total testing capacity and an average testing effort of 0.5 hours per test case, the team can execute 480 test cases (240 hours ÷ 0.5 hours/test case). In this scenario, there are 1,000 test cases, so the team will not be able to execute all test cases within the given time frame.
7. Calculate test coverage: In this scenario, the team can execute 480 test cases out of 1,000 total test cases, so the test coverage is 48% (480 executed test cases ÷ 1,000 total test cases * 100).

The Cost of Coverage

Estimating the difference in cost between 48% and 100% test coverage involves considering several factors such as the potential cost of defects not caught by testing, the cost of fixing defects, and the cost of additional testing efforts required for higher coverage.

Let’s assume the following:

1. Cost of fixing a defect: $1,000
2. Percentage of defects caught at 48% coverage: 70% (this is an assumption; the actual percentage depends on the complexity of the application and the quality of the test cases)
3. Total number of defects in the application: 100 (again, this is an assumption; the actual number of defects will vary)

Now let’s calculate the cost of defects not caught by testing at each coverage level:

At 48% test coverage:

1. Number of defects caught: 70 (70% of 100 defects)
2. Number of defects not caught: 30 (100 – 70)
3. Cost of defects not caught: $30,000 (30 defects * $1,000/defect)

At 100% test coverage:

Assuming that 100% test coverage catches all defects:

1. Number of defects caught: 100
2. Number of defects not caught: 0
3. Cost of defects not caught: $0

Difference in cost due to test coverage:

1. Cost difference due to defects not caught: $30,000 – $0 = $30,000

However, this calculation does not account for the cost of additional testing efforts required to achieve 100% test coverage. To reach 100% coverage, you would need to invest more time and resources, which would also have associated costs.

To calculate the cost of the additional testing efforts required to reach 100% test coverage, we’ll need to consider the following factors:

1. Number of additional test cases: Since we previously calculated that the team could execute 480 test cases in the 2-week time frame, they would need to execute an additional 520 test cases to reach 100% coverage (1,000 total test cases – 480 executed test cases).
2. Average testing effort per test case: As before, the average time to prepare, execute, and report a single test case manually is 30 minutes (0.5 hours).
3. Hourly rate for testers: Assume an average hourly rate of $40 per tester.

Now let’s calculate the cost of additional testing efforts:

1. Additional testing hours needed: 520 additional test cases * 0.5 hours/test case = 260 hours
2. Cost of additional testing efforts: 260 hours * $40/hour = $10,400

So the cost of the additional testing efforts required to reach 100% test coverage would be $10,400. Keep in mind that these calculations are based on hypothetical assumptions and should be tailored to your specific project context. Factors such as tester efficiency, test case complexity, and the accuracy of initial estimations can influence the actual cost of additional testing efforts.

Here’s the rub

The extra effort would cost $10,400 but save $30,000 in potential escaped defects. Justifying the additional $10,400 in testing costs versus the potential $30,000 cost of missed bugs requires a cost-benefit analysis that considers the likelihood of encountering the missed bugs, their impact on the end-users, and the overall business risk.

1. Likelihood of encountering missed bugs: The potential $30,000 cost assumes that all missed bugs will be discovered and need to be fixed. In reality, not all bugs have the same likelihood of being encountered by users, and some may never be discovered or have a negligible impact on the user experience.
2. Impact on end-users: Bugs that are critical to business processes or directly impact the user experience are more likely to result in lost revenue or customer dissatisfaction. If the increased test coverage significantly reduces the risk of such critical bugs, the additional testing cost might be justified.
3. Business risk: Bugs that can cause security breaches, legal issues, or reputational damage may have a much higher cost than the initial $30,000 estimate. In such cases, investing in higher test coverage could be seen as a risk mitigation strategy that helps to prevent more significant financial losses.
4. Cost of fixing defects later: Fixing defects found in production can be much more expensive than fixing them during the development phase. This is because the cost of fixing a defect typically increases as it moves through the software development lifecycle. By investing in more thorough testing, you could save money by identifying and fixing defects earlier in the process.
5. Future maintainability and scalability: High-quality software with fewer defects is generally easier to maintain and scale. Investing in thorough testing can help ensure that the codebase is more stable and less prone to future issues, potentially saving the organization money in the long run.

Ultimately, the decision to invest an additional $10,400 in testing costs will depend on the organization’s risk tolerance, the specific context of the project, and the potential consequences of missed bugs. Consider factors such as the criticality of the application, its user base, and the potential impact of bugs on the organization’s reputation and revenue when making this decision.

Caution

Please note that these calculations are based on hypothetical assumptions and should be tailored to your specific project context. Factors such as the actual cost of fixing defects, the number of defects in the application, and the percentage of defects caught at different test coverage levels can influence the cost difference.