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Fault density (ieee982.1-88)

A program under test can be assessed by counting and classifying the discovered faults by their types. For each fault class, fault density is measured as the ratio between the number of faults found and the size of the program

Life test, reliability evaluation

A statistical estimate of software reliability, which can be obtained by reliability achievement and evaluation, n be used to evaluate a product and decide whether or not testing can be stopped.

Reliability growth models

Reliability growth models provide a prediction of reliability based on the failures observed under reliability achievement and evaluation They assume, in general, that the faults that caused the observed failures have been fixed (although some models also accept imperfect fixes), and thus, on average, the product’s reliability exhibits an increasing trend. There now exist dozens of published models. Many are laid down on some common assumptions, while others differ. Notably, these models are divided into failure-count and time-between-failure models.

Evaluation of the tests performed

Coverage/thoroughness measures (ieee982.1-88)

Several test adequacy criteria require that the test cases systematically exercise a set of elements identified in the program or in the specifications. To evaluate the thoroughness of the executed tests, testers can monitor the elements covered, so that they can dynamically measure the ratio between covered elements and their total number. For example, it is possible to measure the percentage of covered branches in the program flowgraph, or that of the functional requirements exercised among those listed in the specifications document. Code-based adequacy criteria require appropriate instrumentation of the program under test.

Fault seeding

Some faults are artificially introduced into the program before test. When the tests are executed, some of these eeded faults will be revealed, and possibly some faults which were already there will be as well. In theory, depending on which of the artificial faults are discovered, and how many, testing effectiveness can be evaluated, and the remaining number of genuine faults can be estimated. In practice, statisticians question the distribution and representativeness of seeded faults relative to genuine faults and the small sample size on which any extrapolations are based. Some also argue that this technique should be used with great care, since inserting faults into software involves the obvious risk of leaving them there.

Mutation score

In mutation testing, the ratio of killed mutants to the total number of generated mutants can be a measure of the effectiveness of the executed test set.

Comparison and relative effectiveness of different techniques

Several studies have been conducted to compare the relative effectiveness of different test techniques. It is important to be precise as to the property against which the techniques are being assessed; what, for instance, is the exact meaning given to the term “effectiveness”? Possible interpretations are: the number of tests needed to find the first failure, the ratio of the number of faults found through testing to all the faults found during and after testing, or how much reliability was improved. Analytical and empirical comparisons between different techniques have been conducted according to each of the notions of effectiveness specified above.

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Source:  OpenStax, Software engineering. OpenStax CNX. Jul 29, 2009 Download for free at http://cnx.org/content/col10790/1.1
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