Dynamic program analysis

Dynamic program analysis is the analysis of computer software that is performed by executing programs on a real or virtual processor. For dynamic program analysis to be effective, the target program must be executed with sufficient test inputs to produce interesting behavior. Use of software testing measures such as code coverage helps ensure that an adequate slice of the program's set of possible behaviors has been observed. Also, care must be taken to minimize the effect that instrumentation has on the execution (including temporal properties) of the target program. Dynamic analysis is in contrast to static program analysis. Unit tests, integration tests, system tests and acceptance tests use dynamic testing. Dynamic program analysis is the analysis of computer software that is performed by executing programs on a real or virtual processor. For dynamic program analysis to be effective, the target program must be executed with sufficient test inputs to produce interesting behavior. Use of software testing measures such as code coverage helps ensure that an adequate slice of the program's set of possible behaviors has been observed. Also, care must be taken to minimize the effect that instrumentation has on the execution (including temporal properties) of the target program. Dynamic analysis is in contrast to static program analysis. Unit tests, integration tests, system tests and acceptance tests use dynamic testing. Computing the code coverage according to a test suite or a workload is a standard dynamic analysis technique. Fault localization refers to locating the buggy code (for example the buggy statement) according to failing and passing test cases. For example, Tarantula is a well-known fault localization approach based on the covered code. Fault localization illustrates an important property of dynamic analysis: the results on the analysis depend on the considered workload, inputs or test cases. For fault localization, it has been shown that one can refactor the test cases in order to get better results. Daikon is an implementation of dynamic invariant detection. Daikon runs a program, observes the values that the program computes, and then reports properties that were true over the observed executions, and thus likely true over all executions.