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Model–view–controller (MVC) is an architectural pattern used in software engineering. Successful use of the pattern isolates business logic from user interface considerations, resulting in an application where it is easier to modify either the visual appearance of the application or the underlying business rules without affecting the other. In MVC, the model represents the information (the data) of the application; the view corresponds to elements of the user interface such as text, checkbox items, and so forth; and the controller manages the communication of data and the business rules used to manipulate the data to and from the model.

MVC is often seen in web applications, where the view is the actual HTML or XHTML page, and the controller is the code that gathers dynamic data and generates the content within the HTML or XHTML. Finally, the model is represented by the actual content, which is often stored in a database or in XML nodes, and the business rules that transform that content based on user actions.

Mutation testing (or Mutation analysis) is a method of software testing, which involves modifying program's source code in small ways.[1] These, so-called mutations, are based on well-defined mutation operators that either mimic typical programming errors (such as using the wrong operator or variable name) or force the creation of valuable tests (such as driving each expression to zero). The purpose is to help the tester develop effective tests or locate weaknesses in the test data used for the program or in sections of the code that are seldom or never accessed during execution.

For example, consider the following C++ code fragment: if (a && b) c = 1; else c = 0; The condition mutation operator would replace '&&' with '||' and produce the following mutant: if (a || b) c = 1; else c = 0;

Many mutation operators can produce equivalent mutants. For example, consider the following code fragment: int index=0; while (...) { . . .; index++; if (index==10) break; } Boolean relation mutation operator will replace "==" with ">=" and produce the following mutant: int index=0; while (...) { . . .; index++; if (index>=10) break; }

Stochastic optimization (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices, etc.), or in both [1]. The concept contrasts with the deterministic optimization methods, where the values of the objective function are assumed to be exact, and the computation is completely determined by the values sampled so far.