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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 cover almost all possible outputs. 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.

Types of dynamic analysis

Code Coverage Computing the code coverage according to a test suite or a workload is a standard dynamic analysis technique.
Memory Error Detection
Fault localization 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.
Invariant Inference An invariant is a condition that always holds true at certain points in the program. Invariant inference tools are mainly used for debugging programs in late development, or checking modifications to existing code. .
Security analysis
Concurrency errors
Program slicing For a given subset of a program’s behavior, program slicing consists of reducing the program to the minimum form that still produces the selected behavior. The reduced program is called a “slice” and is a faithful representation of the original program within the domain of the specified behavior subset. Generally, finding a slice is an unsolvable problem, but by specifying the target behavior subset by the values of a set of variables, it is possible to obtain approximate slices using a data-flow algorithm. These slices are usually used by developers during debugging to locate the source of errors.
Performance Analysis Most performance analysis tools use dynamic program analysis techniques.

Resource

Dynamic Type Checking

Dynamic type checking is the process of verifying the type safety of a program at runtime. Implementations of dynamically type-checked languages generally associate each runtime object with a type tag (i.e., a reference to a type) containing its type information. This runtime type information (RTTI) can also be used to implement dynamic dispatch, late binding, downcasting, reflection, and similar features.

Most type-safe languages include some form of dynamic type checking, even if they also have a static type checker.[citation needed] [8] The reason for this is that many useful features or properties are difficult or impossible to verify statically. For example, suppose that a program defines two types, A and B, where B is a subtype of A. If the program tries to convert a value of type A to type B, which is known as downcasting, then the operation is legal only if the value being converted is actually a value of type B. Thus, a dynamic check is needed to verify that the operation is safe. This requirement is one of the criticisms of downcasting.

By definition, dynamic type checking may cause a program to fail at runtime. In some programming languages, it is possible to anticipate and recover from these failures. In others, type-checking errors are considered fatal.

Programming languages that include dynamic type checking but not static type checking are often called "dynamically-typed programming languages". For a list of such languages, see the category for dynamically-typed programming languages.

Tools

Kieker

Application-level monitoring and dynamic analysis of software systems are a basis for various tasks in software engineering research, such as performance evaluation and reverse engineering. The Kieker framework provides monitoring, analysis, and visualization support for these purposes. It commenced in 2006, and grew toward a high-quality open-source software that has been employed in a variety of software engineering research projects over the last decade. Several research groups constitute the open-source community to advance the Kieker framework. In this paper, we review Kieker’s history, development, and impact both in research and technology transfer with industry.

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