Software Engineering-Metrics for Testing

Although much has been written on software metrics for testing , the majority of metrics proposed focus on the process of testing, not the technical characteristics of the tests themselves. In general, testers must rely on analysis, design, and code metrics to guide them in the design and execution of test cases.

Function-based metrics can be used as a predictor for overall testing effort. Various project-level characteristics (e.g., testing effort and time, errors uncovered, number of test cases produced) for past projects can be collected and correlated with the number of FP produced by a project team. The team can then project “expected values” of these characteristics for the current project.

The bang metric can provide an indication of the number of test cases required by examining the primitive measures . The number of functional primitives (FuP), data elements (DE), objects (OB), relationships (RE), states (ST), and transitions (TR) can be used to project the number and types of black-box and white-box tests for the software. For example, the number of tests associated with the human/computer interface can be estimated by (1) examining the number of transitions (TR) contained in the state transition representation of the HCI and evaluating the tests required to exercise each transition; (2) examining the number of data objects (OB) that move across the interface, and (3) the number of data elements that are input or output.

Architectural design metrics provide information on the ease or difficulty associated with integration testing  and the need for specialized testing software (e.g., stubs and drivers). Cyclomatic complexity (a component-level design metric) lies at the core of basis path testing, a test case design method . In addition, cyclomatic complexity can be used to target modules as candidates for extensive unit testing. Modules with high cyclomatic complexity are more likely to be error prone than modules whose cyclomatic complexity is lower. For this reason, the tester should expend above average effort to uncover errors in such modules before they are integrated in a system. Testing effort can also be estimated using metrics derived from Halstead measures. Using the definitions for program volume, V, and program level, PL, software science effort, e, can be computed as

                           PL = 1/[(n1/2)•(N2/n2)]       (1)
                           e = V/PL                             (2)
The percentage of overall testing effort to be allocated to a module k can be estimated using the following relationship:

      percentage of testing effort (k) = e(k)/ e(i)     (3)
where e(k) is computed for module k using Equations (1) and (2) and the summation in the denominator of Equation (4) is the sum of software science effort across all modules of the system.

As tests are conducted, three different measures provide an indication of testing completeness. A measure of the breath of testing provides an indication of how many requirements (of the total number of requirements) have been tested. This provides an indication of the completeness of the test plan. Depth of testing is a measure of the percentage of independent basis paths covered by testing versus the total number of basis paths in the program. A reasonably accurate estimate of the number of basis paths can be computed by adding the cyclomatic complexity of all program modules. Finally, as tests are conducted and error data are collected, fault profiles may be used to rank and categorize errors uncovered. Priority indicates the severity of the problem. Fault categories provide a description of an error so that statistical error analysis can be conducted.
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