In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms—the amount of time, storage, or other resources needed to execute them. Usually, this involves determining a function that relates the size of an algorithm's input to the number of steps it takes or the number of storage locations it uses. An algorithm is said to be efficient when this function's values are small, or grow slowly compared to a growth in the size of the input. Different inputs of the same size may cause the algorithm to have different behavior, so best, worst and average case descriptions might all be of practical interest. When not otherwise specified, the function describing the performance of an algorithm is usually an upper bound, determined from the worst case inputs to the algorithm.
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem.
In software engineering, code coverage, also called test coverage, is a percentage measure of the degree to which the source code of a program is executed when a particular test suite is run. A program with high code coverage has more of its source code executed during testing, which suggests it has a lower chance of containing undetected software bugs compared to a program with low code coverage. Many different metrics can be used to calculate test coverage. Some of the most basic are the percentage of program subroutines and the percentage of program statements called during execution of the test suite.
Software testing is the act of checking whether software satisfies expectations.
Linear programming (LP), also called linear optimization, is a method to achieve the best outcome in a mathematical model whose requirements and objective are represented by linear relationships. Linear programming is a special case of mathematical programming.
In computer science, algorithmic efficiency is a property of an algorithm which relates to the amount of computational resources used by the algorithm. Algorithmic efficiency can be thought of as analogous to engineering productivity for a repeating or continuous process.
Unit testing, a.k.a. component or module testing, is a form of software testing by which isolated source code is tested to validate expected behavior.
In theoretical computer science, an algorithm is correct with respect to a specification if it behaves as specified. Best explored is functional correctness, which refers to the input-output behavior of the algorithm: for each input it produces an output satisfying the specification.
Test-driven development (TDD) is a way of writing code that involves writing an automated unit-level test case that fails, then writing just enough code to make the test pass, then refactoring both the test code and the production code, then repeating with another new test case.
In mathematics, when a mathematical phenomenon runs counter to some intuition, then the phenomenon is sometimes called pathological. On the other hand, if a phenomenon does not run counter to intuition, it is sometimes called well-behaved or nice. These terms are sometimes useful in mathematical research and teaching, but there is no strict mathematical definition of pathological or well-behaved.
In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor.
A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output are random variables.
White-box testing is a method of software testing that tests internal structures or workings of an application, as opposed to its functionality. In white-box testing, an internal perspective of the system is used to design test cases. The tester chooses inputs to exercise paths through the code and determine the expected outputs. This is analogous to testing nodes in a circuit, e.g. in-circuit testing (ICT). White-box testing can be applied at the unit, integration and system levels of the software testing process. Although traditional testers tended to think of white-box testing as being done at the unit level, it is used for integration and system testing more frequently today. It can test paths within a unit, paths between units during integration, and between subsystems during a system–level test. Though this method of test design can uncover many errors or problems, it has the potential to miss unimplemented parts of the specification or missing requirements. Where white-box testing is design-driven, that is, driven exclusively by agreed specifications of how each component of software is required to behave, white-box test techniques can accomplish assessment for unimplemented or missing requirements.
In mathematics, approximation theory is concerned with how functions can best be approximated with simpler functions, and with quantitatively characterizing the errors introduced thereby. What is meant by best and simpler will depend on the application.
In computer science, fault injection is a testing technique for understanding how computing systems behave when stressed in unusual ways. This can be achieved using physical- or software-based means, or using a hybrid approach. Widely studied physical fault injections include the application of high voltages, extreme temperatures and electromagnetic pulses on electronic components, such as computer memory and central processing units. By exposing components to conditions beyond their intended operating limits, computing systems can be coerced into mis-executing instructions and corrupting critical data.
In computing, computer performance is the amount of useful work accomplished by a computer system. Outside of specific contexts, computer performance is estimated in terms of accuracy, efficiency and speed of executing computer program instructions. When it comes to high computer performance, one or more of the following factors might be involved:
Richard Jay Lipton is an American computer scientist who is Associate Dean of Research, Professor, and the Frederick G. Storey Chair in Computing in the College of Computing at the Georgia Institute of Technology. He has worked in computer science theory, cryptography, and DNA computing.
In utility and industrial electric power transmission and distribution systems, a numerical relay is a computer-based system with software-based protection algorithms for the detection of electrical faults. Such relays are also termed as microprocessor type protective relays. They are functional replacements for electro-mechanical protective relays and may include many protection functions in one unit, as well as providing metering, communication, and self-test functions.
In software engineering, a microservice architecture is an architectural pattern that arranges an application as a collection of loosely coupled, fine-grained services, communicating through lightweight protocols. One of its goals is to enable teams to develop and deploy their services independently. This is achieved by reducing several dependencies in the codebase, allowing developers to evolve their services with limited restrictions, and hiding additional complexity from users. Consequently, organizations can develop software with rapid growth and scalability, as well as use off-the-shelf services more easily. Communication requirements are reduced. These benefits come with the cost of maintaining decoupling, so a microservice architecture may be suitable only if the application is too complex to manage as a monolith. Interfaces need to be designed carefully and treated as public API. One technique used is having multiple interfaces on the same service or multiple versions of the same service to avoid disrupting existing users of the code.
This glossary of computer science is a list of definitions of terms and concepts used in computer science, its sub-disciplines, and related fields, including terms relevant to software, data science, and computer programming.
