Quantitative Paradigm of Software Reliability as Content Relevance

Quantitative Paradigm of Software Reliability as Content Relevance Yuri Arkhipkin Moscow, Russia aryur@yandex.ru This paper presents a quantitative approach to software reliability and content relevance definitions validated by the systems’ potential reliability law. Thus it is argued for the unified math nature or quantitative paradigm of software reliability and content relevance. This paradigm integrates subject matter data and quantitative software reliability metrics and is viewed as a foundation of the software engineering that provides continuous reliability evaluations throughout software development process, thus accounting and tracing quantitative software reliability requirements from customer to product. This paradigm integrates subject matter data and quantitative content relevance metrics and is viewed as a foundation of the content relevance engineering that provides continuous relevance evaluations throughout content engineering process, thus, making possible to trace content relevance requirements from customer to product. This integrated development environment is to be assured and supported by quantification grammar as a foundation of software reliability quantification programming language and content relevance quantification markup language. 1. Introduction Much research was done on models approaching software reliability quantification. The results seem to be of poor satisfaction despite of the increasing number of these models. The lack of explicit evaluations of software elements’ failure probability may be considered as one of the main problems in software reliability quantification. Software elements’ failure data is yielded while testing (executing) software for a vast field of subject applications. This data may be considered as an ad hoc data much depending on developer’s skill and software testing skill in particular. Software testing in general may be considered as a trial failure process of sensitizing software elements (sites) to define whether yielded results are true or faulty. Much research was also done on models approaching content relevance quantification. The results seem to be of not enough satisfaction despite of the increasing number of these models. Content’s grammar variety may be considered as one of the main problems in content relevance quantification. Content irrelevance (failure) data is yielded due to query occurrence (generation) through content searching, thus providing data for query’s terms refinements and (or) search engine’s improvements. So this data generally may be considered as an ad hoc data much depending on search engine’s developer skill and query generation (testing) skill in particular. Content searching in general may be considered as a trial failure process of sensitizing content terms to define whether this content is relevant to the terms of the query or not. This paper offers to break through the quantification problems of software reliability and content relevance engineering by approaching any digital content as a trial failure system regardless of its grammar and subject matter applications. Chapter 2 introduces briefly some mathematics of the systems’ potential reliability law proved by B. S. Fleishman [1]. This law validates the systems’ failure intensity quantitative ranges depending on known potential operating elements’ number as a part of system elements’ total number and their mean operating probability. Chapter 3 presents the quantitative approach to the software reliability engineering validated by systems’ potential reliability law. Software site’s operating probability is considered to be equal to the site’s probability occurrence or potential occurrence. This probability may be evaluated at any software development cycle. Developer in general needs no external statistic data to monitor the achieved quantitative reliability level of the software project. Chapter 4 presents the quantitative approach to the content relevance engineering validated by systems’ potential reliability law. Content element’s operating (sensitizing sense) probability is considered to be equal to the term’s content frequency. This frequency may be evaluated at any cycle of the content development. Developer in general needs no external statistic data to index the quantitative relevance level of the content project. Chapter 5 concludes with preliminary approach to software reliability quantification grammar to be implemented as a language of the integrated development environment for a product lifecycle solution in software engineering. The pragmatics of the presented paradigm may be defined by its validity and verifiability that may be explicitly quantified for the vast field of system engineering applications. 2. Potential Reliability of a Trial Failure System At every time moment the system’s elements belong to either operating or failure state. Moreover the conversion occurs instantly from ope

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