For example, given two software projects of similar complexity and size, the appropriate test effort for one project might be very large if it was for life-critical medical equipment software, but might be much smaller for the other project if it was for a low-cost computer game. A test estimation approach that only considered size and complexity might be appropriate for one project but not for the other.

Following are some approaches to consider.

Implicit Risk Context Approach:
A typical approach to test estimation is for a project manager or QA manager to implicitly use risk context, in combination with past personal experiences in the organization, to choose a level of resources to allocate to testing. In many organizations, the 'risk context' is assumed to be similar from one project to the next, so there is no explicit consideration of risk context. (Risk context might include factors such as the organization's typical software quality levels, the software's intended use, the experience level of developers and testers, etc.) This is essentially an intuitive guess based on experience.

Metrics-Based Approach:
A useful approach is to track past experience of an organization's various projects and the associated test effort that worked well for projects. Once there is a set of data covering characteristics for a reasonable number of projects, then this 'past experience' information can be used for future test project planning. (Determining and collecting useful project metrics over time can be an extremely difficult task.) For each particular new project, the 'expected' required test time can be adjusted based on whatever metrics or other information is available, such as function point count, number of external system interfaces, unit testing done by developers, risk levels of the project, etc. In the end, this is essentially 'judgement based on documented experience', and is not easy to do successfully.

Test Work Breakdown Approach:
Another common approach is to decompose the expected testing tasks into a collection of small tasks for which estimates can, at least in theory, be made with reasonable accuracy. This of course assumes that an accurate and predictable breakdown of testing tasks and their estimated effort is feasible. In many large projects, this is not the case. For example, if a large number of bugs are being found in a project, this will add to the time required for testing, retesting, bug analysis and reporting. It will also add to the time required for development, and if development schedules and efforts do not go as planned, this will further impact testing

For example, given two software projects of similar complexity and size, the appropriate test effort for one project might be very large if it was for life-critical medical equipment software, but might be much smaller for the other project if it was for a low-cost computer game. A test estimation approach that only considered size and complexity might be appropriate for one project but not for the other.

Following are some approaches to consider.

Implicit Risk Context Approach:
A typical approach to test estimation is for a project manager or QA manager to implicitly use risk context, in combination with past personal experiences in the organization, to choose a level of resources to allocate to testing. In many organizations, the 'risk context' is assumed to be similar from one project to the next, so there is no explicit consideration of risk context. (Risk context might include factors such as the organization's typical software quality levels, the software's intended use, the experience level of developers and testers, etc.) This is essentially an intuitive guess based on experience.

Metrics-Based Approach:
A useful approach is to track past experience of an organization's various projects and the associated test effort that worked well for projects. Once there is a set of data covering characteristics for a reasonable number of projects, then this 'past experience' information can be used for future test project planning. (Determining and collecting useful project metrics over time can be an extremely difficult task.) For each particular new project, the 'expected' required test time can be adjusted based on whatever metrics or other information is available, such as function point count, number of external system interfaces, unit testing done by developers, risk levels of the project, etc. In the end, this is essentially 'judgement based on documented experience', and is not easy to do successfully.

Test Work Breakdown Approach:
Another common approach is to decompose the expected testing tasks into a collection of small tasks for which estimates can, at least in theory, be made with reasonable accuracy. This of course assumes that an accurate and predictable breakdown of testing tasks and their estimated effort is feasible. In many large projects, this is not the case. For example, if a large number of bugs are being found in a project, this will add to the time required for testing, retesting, bug analysis and reporting. It will also add to the time required for development, and if development schedules and efforts do not go as planned, this will further impact testing