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Learnability
Learnability is a quality of products and interfaces that allows users to quickly become familiar with them and able to make good use of all their features and capabilities. Software testing In software testing learnability, according to ISO/IEC 9126, is the capability of a software product to enable the user to learn how to use it. Learnability may be considered as an aspect of usability, and is of major concern in the design of complex software applications. Learnability is defined in the ''Standard glossary of terms used in software testing'' published by the International Software Testing Qualifications Board. Computational learning theory In computational learning theory, learnability is the mathematical analysis of machine learning. It is also employed in language acquisition in arguments within linguistics. Frameworks include: * Language identification in the limit proposed in 1967 by E. Mark Gold. Subsequently known as Algorithmic learning theory. * Probably approximate ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Identification In The Limit
Language identification in the limit is a formal model for inductive inference of formal languages, mainly by computers (see machine learning and induction of regular languages). It was introduced by E. Mark Gold in a technical report and a journal article with the same title. In this model, a ''teacher'' provides to a ''learner'' some ''presentation'' (i.e. a sequence of strings) of some formal language. The learning is seen as an infinite process. Each time the learner reads an element of the presentation, it should provide a ''representation'' (e.g. a formal grammar) for the language. Gold defines that a learner can ''identify in the limit'' a class of languages if, given any presentation of any language in the class, the learner will produce only a finite number of wrong representations, and then stick with the correct representation. However, the learner need not be able to announce its correctness; and the teacher might present a counterexample to any representation arbitrar ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Usability
Usability can be described as the capacity of a system to provide a condition for its users to perform the tasks safely, effectively, and efficiently while enjoying the experience. In software engineering, usability is the degree to which a software can be used by specified consumers to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use. The object of use can be a software application, website, book, tool, machine, process, vehicle, or anything a human interacts with. A usability study may be conducted as a primary job function by a ''usability analyst'' or as a secondary job function by designers, technical writers, marketing personnel, and others. It is widely used in consumer electronics, communication, and knowledge transfer objects (such as a cookbook, a document or online help) and mechanical objects such as a door handle or a hammer. Usability includes methods of measuring usability, such as needs analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probably Approximately Correct Learning
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant.L. Valiant. A theory of the learnable.' Communications of the ACM, 27, 1984. In this framework, the learner receives samples and must select a generalization function (called the ''hypothesis'') from a certain class of possible functions. The goal is that, with high probability (the "probably" part), the selected function will have low generalization error (the "approximately correct" part). The learner must be able to learn the concept given any arbitrary approximation ratio, probability of success, or distribution of the samples. The model was later extended to treat noise (misclassified samples). An important innovation of the PAC framework is the introduction of computational complexity theory concepts to machine learning. In particular, the learner is expected to find efficient functions (tim ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ISO/IEC 9126
ISO/IEC 9126 ''Software engineering — Product quality'' was an international standard for the evaluation of software quality. It has been replaced by ISO/IEC 25010:2011. The fundamental objective of the ISO/IEC 9126 standard is to address some of the well known human biases that can adversely affect the delivery and perception of a software development project. These biases include changing priorities after the start of a project or not having any clear definitions of "success". By clarifying, then agreeing on the project priorities and subsequently converting abstract priorities (compliance) to measurable values (output data can be validated against schema X with zero intervention), ISO/IEC 9126 tries to develop a common understanding of the project's objectives and goals. The standard is divided into four parts: * quality model * external metrics * internal metrics * quality in use metrics. Quality The quality model presented in the first part of the standard, ISO/IEC 912 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Learning Theory
In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms. Overview Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given samples that are labeled in some useful way. For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible. The algorithm takes these previously labeled samples and uses them to induce a classifier. This classifier is a function that assigns labels to samples, including samples that have not been seen previously by the algorithm. The goal of the supervised learning algorithm is to optimize some measure of performance such as minimizing the number of mistakes made on new samples. In addition to performance bounds, computational learning theory studies the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithmic Learning Theory
Algorithmic learning theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and algorithmic inductive inference. Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis. Both algorithmic and statistical learning theory are concerned with machine learning and can thus be viewed as branches of computational learning theory. Distinguishing characteristics Unlike statistical learning theory and most statistical theory in general, algorithmic learning theory does not assume that data are random samples, that is, that data points are independent of each other. This makes the theory suitable for domains where observations are (relatively) noise-free but not random, such as language learning and automated scientific discovery. The fundamental concept of algorithmic learning theory is learning in the limit: as the number of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Software Testing
Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but not necessarily limited to: * analyzing the product requirements for completeness and correctness in various contexts like industry perspective, business perspective, feasibility and viability of implementation, usability, performance, security, infrastructure considerations, etc. * reviewing the product architecture and the overall design of the product * working with product developers on improvement in coding techniques, design patterns, tests that can be written as part of code based on various techniques like boundary conditions, etc. * executing a program or application with the intent of examining behavior * reviewing the deployment infrastructure a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
International Software Testing Qualifications Board
The International Software Testing Qualifications Board (ISTQB) is a software testing certification board that operates internationally. Founded in Edinburgh in November 2002, the ISTQB is a non-profit association legally registered in Belgium. ISTQB Certified Tester is a standardized qualification for software testers and the certification is offered by the ISTQB. The qualifications are based on a syllabus, and there is a hierarchy of qualifications and guidelines for accreditation and examination. More than 1 million ISTQB exams have been delivered and over 721,000 certifications issued; the ISTQB consists of 67 member boards worldwide representing more than 100 countries as of April 2021. Product portfolio Current ISTQB product portfolio follows a matrix approach characterized by * Levels, that identify progressively increasing learning objectives ** Foundation ** Advanced ** Expert * Streams, that identify clusters of certification modules: ** Core ** Agile ** Specialist ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Acquisition
Language acquisition is the process by which humans acquire the capacity to perceive and comprehend language (in other words, gain the ability to be aware of language and to understand it), as well as to produce and use words and sentences to communicate. Language acquisition involves structures, rules and representation. The capacity to use language successfully requires one to acquire a range of tools including phonology, morphology, syntax, semantics, and an extensive vocabulary. Language can be vocalized as in speech, or manual as in sign. Human language capacity is represented in the brain. Even though human language capacity is finite, one can say and understand an infinite number of sentences, which is based on a syntactic principle called recursion. Evidence suggests that every individual has three recursive mechanisms that allow sentences to go indeterminately. These three mechanisms are: ''relativization'', ''complementation'' and ''coordination''. There are two m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Leslie Valiant
Leslie Gabriel Valiant (born 28 March 1949) is a British American computer scientist and computational theorist. He was born to a chemical engineer father and a translator mother. He is currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University. Valiant was awarded the Turing Award in 2010, having been described by the A.C.M. as a heroic figure in theoretical computer science and a role model for his courage and creativity in addressing some of the deepest unsolved problems in science; in particular for his "striking combination of depth and breadth". Education Valiant was educated at King's College, Cambridge, Imperial College London, and the University of Warwick where he received a PhD in computer science in 1974. Research and career Valiant is world-renowned for his work in theoretical computer science. Among his many contributions to complexity theory, he introduced the notion of #P-completeness ("sharp-P comple ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
