Relevant bibliographies by topics / Component software – Reliability

Academic literature on the topic 'Component software – Reliability'

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Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Component software – Reliability"

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S, R. Chinnaiyan, and Somasundaram . "Reliability of Component Based Software with Similar Software Components � a Review." i-manager's Journal on Software Engineering 5, no. 2 (December 15, 2010): 44–49. http://dx.doi.org/10.26634/jse.5.2.1335.

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ZHOU, Na-qin. "Reliability model of component-based software." Journal of Computer Applications 28, no. 6 (June 20, 2008): 1630–31. http://dx.doi.org/10.3724/sp.j.1087.2008.01630.

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Shuchen, Liu, and Cao Wei. "Research of Component Software Reliability Estimation." Open Cybernetics & Systemics Journal 9, no. 1 (April 17, 2015): 199–203. http://dx.doi.org/10.2174/1874110x01509010199.

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KRISTIANSEN, MONICA, RUNE WINTHER, and BENT NATVIG. "ON COMPONENT DEPENDENCIES IN COMPOUND SOFTWARE." International Journal of Reliability, Quality and Safety Engineering 17, no. 05 (October 2010): 465–93. http://dx.doi.org/10.1142/s0218539310003895.

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Predicting the reliability of software systems based on a component approach is inherently difficult, in particular due to failure dependencies between the software components. Since it is practically difficult to include all component dependencies in a system's reliability calculation, a more viable approach would be to include only those dependencies that have a significant impact on the assessed system reliability. This paper starts out by defining two new concepts: data-serial and data-parallel components. Then, this paper illustrates how the components' marginal reliabilities put direct restrictions on the components' conditional probabilities, and proves that the degrees of freedom are much fewer than first anticipated when it comes to conditional probabilities. At last, a test system, consisting of five components, is investigated to identify possible rules for selecting the most important component dependencies. To do this, three different techniques are applied: (1) direct calculation, (2) Birnbaum's measure and (3) Principal Component Analysis (PCA). The results from the analyses clearly show that including partial dependency information may give substantial improvements in the reliability predictions, compared to assuming independence between all software components. The analyses also indicate that including only dependencies between data-parallel components may give predictions close to the system's true failure probability, as long as the dependency between the most unreliable components is included. Including only dependencies between data-serial components may however result in predictions even worse than by assuming independence between all software components.
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Tomar, Dimpal, and Pradeep Tomar. "New component-based reliability model to predict the reliability of component-based software." International Journal of Reliability and Safety 13, no. 1/2 (2019): 83. http://dx.doi.org/10.1504/ijrs.2019.097018.

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Tomar, Dimpal, and Pradeep Tomar. "New component-based reliability model to predict the reliability of component-based software." International Journal of Reliability and Safety 13, no. 1/2 (2019): 83. http://dx.doi.org/10.1504/ijrs.2019.10017892.

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Yakovyna, Vitalij S. "COMPONENT BASED HIGH-ORDER SOFTWARE RELIABILITY MODELS." ELECTRICAL AND COMPUTER SYSTEMS 19, no. 95 (July 2, 2015): 252–55. http://dx.doi.org/10.15276/eltecs.19.95.2015.57.

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HOU, Chun-Yan, Gang CUI, and Hong-Wei LIU. "Rate-Based Component Software Reliability Process Simulation." Journal of Software 22, no. 11 (November 14, 2011): 2749–59. http://dx.doi.org/10.3724/sp.j.1001.2011.03930.

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Reussner, Ralf H., Heinz W. Schmidt, and Iman H. Poernomo. "Reliability prediction for component-based software architectures." Journal of Systems and Software 66, no. 3 (June 2003): 241–52. http://dx.doi.org/10.1016/s0164-1212(02)00080-8.

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Wang, Qiang, Yang Lu, Zijun Xu, and Jianghong Han. "Software reliability model for component interaction mode." Journal of Electronics (China) 28, no. 4-6 (November 2011): 632–42. http://dx.doi.org/10.1007/s11767-012-0616-2.

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Dissertations / Theses on the topic "Component software – Reliability"

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Mason, Dave. "Probabilistic Program Analysis for Software Component Reliability." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/1059.

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Components are widely seen by software engineers as an important technology to address the "software crisis''. An important aspect of components in other areas of engineering is that system reliability can be estimated from the reliability of the components. We show how commonly proposed methods of reliability estimation and composition for software are inadequate because of differences between the models and the actual software systems, and we show where the assumptions from system reliability theory cause difficulty when applied to software. This thesis provides an approach to reliability that makes it possible, if not currently plausible, to compose component reliabilities so as to accurately and safely determine system reliability. Firstly, we extend previous work on input sub-domains, or partitions, such that our sub-domains can be sampled in a statistically sound way. We provide an algorithm to generate the most important partitions first, which is particularly important when there are an infinite number of input sub-domains. We combine analysis and testing to provide useful reliabilities for the various input sub-domains of a system, or component. This provides a methodology for calculating true reliability for a software system for any accurate statistical distribution of input values. Secondly, we present a calculus for probability density functions that permits accurately modeling the input distribution seen by each component in the system - a critically important issue in dealing with reliability of software components. Finally, we provide the system structuring calculus that allows a system designer to take components from component suppliers that have been built according to our rules and to determine the resulting system reliability. This can be done without access to the actual components. This work raises many issues, particularly about scalability of the proposed techniques and about the ability of the system designer to know the input profile to the level and kind of accuracy required. There are also large classes of components where the techniques are currently intractable, but we see this work as an important first step.
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Mason, David Victor. "Probabilistic program analysis for software component reliability." Waterloo, Ont. : University of Waterloo, 2002. http://etd.uwaterloo.ca/etd/dmason2002.pdf.

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Thesis (Ph.D.)--University of Waterloo, 2002.
"A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Doctor of Philosophy in Computer Science". Includes bibliographical references. Also available in microfiche format.
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Kamavaram, Sunil Kumar. "Uncertainty analysis in software reliability of component-based systems." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3300.

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Thesis (M.S.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains xii, 81 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 78-81).
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Smith, William B. "Early component-based reliability assessment using UML based software models." Morgantown, W. Va. : [West Virginia University Libraries], 2002. http://etd.wvu.edu/templates/showETD.cfm?recnum=2634.

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Thesis (M.S.)--West Virginia University, 2002.
Title from document title page. Document formatted into pages; contains vii, 62 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 26-27).
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Zhu, Liming Computer Science &amp Engineering Faculty of Engineering UNSW. "Software architecture evaluation for framework-based systems." Awarded by:University of New South Wales. Computer Science and Engineering, 2007. http://handle.unsw.edu.au/1959.4/28250.

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Complex modern software is often built using existing application frameworks and middleware frameworks. These frameworks provide useful common services, while simultaneously imposing architectural rules and constraints. Existing software architecture evaluation methods do not explicitly consider the implications of these frameworks for software architecture. This research extends scenario-based architecture evaluation methods by incorporating framework-related information into different evaluation activities. I propose four techniques which target four different activities within a scenario-based architecture evaluation method. 1) Scenario development: A new technique was designed aiming to extract general scenarios and tactics from framework-related architectural patterns. The technique is intended to complement the current scenario development process. The feasibility of the technique was validated through a case study. Significant improvements of scenario quality were observed in a controlled experiment conducted by another colleague. 2) Architecture representation: A new metrics-driven technique was created to reconstruct software architecture in a just-in-time fashion. This technique was validated in a case study. This approach has significantly improved the efficiency of architecture representation in a complex environment. 3) Attribute specific analysis (performance only): A model-driven approach to performance measurement was applied by decoupling framework-specific information from performance testing requirements. This technique was validated on two platforms (J2EE and Web Services) through a number of case studies. This technique leads to the benchmark producing more representative measures of the eventual application. It reduces the complexity behind the load testing suite and framework-specific performance data collecting utilities. 4) Trade-off and sensitivity analysis: A new technique was designed seeking to improve the Analytical Hierarchical Process (AHP) for trade-off and sensitivity analysis during a framework selection process. This approach was validated in a case study using data from a commercial project. The approach can identify 1) trade-offs implied by an architecture alternative, along with the magnitude of these trade-offs. 2) the most critical decisions in the overall decision process 3) the sensitivity of the final decision and its capability for handling quality attribute priority changes.
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Braz, Alan 1980. "Método Ágil aplicado ao desenvolvimento de software confiável baseado em componentes." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275637.

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Orientador: Cecília Mary Fischer Rubira
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-23T09:09:24Z (GMT). No. of bitstreams: 1 Braz_Alan_M.pdf: 1903353 bytes, checksum: 9bff9aefdcc11d6d8fe46490302d6291 (MD5) Previous issue date: 2013
Resumo: Os Métodos Ágeis, ou Desenvolvimento Ágil de Software (DAS), tem se popularizado, na última década, por meio de métodos como Extreme Programming (XP) e Scrum e isso fez com que fossem aplicadas no desenvolvimento de sistemas computacionais de diversos tamanhos, complexidades técnica e de domínio, e de rigor quanto à confiabilidade. Esse fato evidencia a necessidade de processos de desenvolvimento de software que sejam mais rigorosos e que possuam uma quantidade adequada de modelagem e documentação, em especial no que concerne ao projeto arquitetural, com o objetivo de garantir maior qualidade no seu resultado final. A confiabilidade pode ser alcançada adicionando elementos de tratamento de exceções às fases iniciais do processo de desenvolvimento e à reutilização de componentes. O tratamento de exceções tem sido uma técnica muito utilizada na verificação e na depuração de erros em sistemas de software. O MDCE+ é um método que auxilia a modelagem do comportamento excepcional de sistemas baseados em componentes que, por ser centrado na arquitetura, melhora a definição e a análise do fluxo de exceções entre os componentes do sistema. Este trabalho propõe uma solução para guiar o desenvolvimento de sistemas confiáveis baseados em componentes por meio da adição de práticas do MDCE+ ao Scrum, resultando no método Scrum+CE (Scrum com Comportamento Excepcional). Esse processo passa a expor os requisitos excepcionais em nível das Estórias de Usuário, adiciona testes de aceitação mais detalhados, obriga a criação do artefato de Arquitetura Inicial e adiciona um novo papel de Dono da Arquitetura. Como forma de avaliar esse método proposto, foi realizado um experimento controlado com três equipes, que desenvolveram um sistema com requisitos de confiabilidade, utilizando Scrum e Scrum+CE. Foram coletadas métricas para comparar a eficiência do novo processo e o resultado obtido, com a utilização do Scrum+CE, foi à produção de software com melhor qualidade, porém com menor número de funcionalidades
Abstract: Agile Software Development (ASD) has been on mainstream through methodologies such as Extreme Programming (XP) and Scrum in the last decade enabling them to be applied in the development of computer systems of various size, technical and domain complexity and degress of reliability. This fact highlights the need for software development processes that are accurate and have an adequate amount of modeling and documentation, especially regarding the architectural design, aiming to increase the quality of the end result. The reliability can be achieved by adding elements of exception handling at early stages of development and through components reuse. Exception handling has been a widely used technique in detecting and fixing errors in software systems. The MDCE+ is a method that assists exceptional behavior modeling at components based systems, which is architecture-centric what improves the definition and flow analysis of exceptions between system components. This paper proposes a solution to guide the development of reliable systems based on components by adding MDCE+ practices to Scrum, resulting in the Scrum+CE method (Scrum with Exceptional Behavior). This process exposes the exceptional requirements, at the User Stories level, documents acceptance tests with more details, requires the creation of a high-level architecture artifact and adds a new role of Architecture Owner. In order to evaluate this proposed method, a controlled experiment was conducted with three teams, who developed a system with reliability requirements using Scrum and Scrum+CE. We collected metrics to compare the efficiency of the new process and the result was the production of software with better quality but with less features using Scrum+CE
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
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Duarte, André Tiago Oliveira da Silva. "Software Repository Mining Analytics to Estimate Software Component Reliability." Dissertação, 2016. https://repositorio-aberto.up.pt/handle/10216/89450.

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Dada a crescente necessidade de identificar a localização dos erros no código fonte de software, de forma a facilitar o trabalho dos programadores e a acelerar o processo de desenvolvimento, muitos avanços têm sido feitos na sua automação.Existem três abordagens principais: Program-spectra based (PSB), Model-based diagnosis (MDB) e Program slicing.Barinel, solução que integra tanto o PSB como o MDB, é, até hoje, com base na investigação feita, a que apresenta melhores resultados. Contudo, a ordenação de conjuntos de candidatos (componentes faltosos) não tem em conta a verdadeira qualidade do componente em causa, mas sim o conjunto de valores que maximizam a probabilidade do conjunto (Maximum Likehood Estimation - MLE), devido à dificuldade da sua determinação.Com esta tese pretende-se colmatar esta falha e contribuir para uma melhor ordenação dos conjuntos, classificando, com recurso a técnicas de Machine Learning como Naive Bayes, Support Vector Machines (SVM) ou Random Forests, a qualidade e fiabilidade de cada componente, através das informações disponíveis no sistema de controlo de versões (Software Repository Mining), neste caso Git, como por exemplo: número de vezes que foi modificado, número de contribuidores, data de última alteração, nome de últimos contribuidores e tamanho das alterações.A investigação já feita, revelou a existência de algumas soluções de análise preditiva de software, como BugCache, FixCache e Change Classification, capazes de identificar componentes com grande probabilidade de falhar e de classificar as revisões (commits) como faltosas ou não, mas nenhuma soluciona o problema.Este trabalho visa também a integração com o Crowbar e a contribuição para a sua possível comercialização.
Given the rising necessity of identifying errors on the source code of software, in order to make the developers work easier and to speed up the development process, many progresses have been made in its automation.There are three main approaches: Program-spectra based (PSB), Model-based diagnosis (MDB) and Program slicing.Barinel, solution that integrates both PSB and MDB, is, until now, to our knowledge, the option that guarantees the best results. Despite this, the candidates (faulty components) set order doesn't take into account the real quality of the given component. With this thesis we want to fix this issue and contribute for a better candidates ordered set, classifying the quality and reliability of each component, using Machine Learning techniques such as Decision Trees, Support Vector Machines or Random Forests with the information extracted from Git, like: number of times it was modified, number of contributors, date of last change and size of those changes.The research revealed the existence of some software predictive analysis solutions, such as BugCache, FixCache and Change Classification, capable of identifying the components with a high probability of failure and of classifying the changes (commits) as faulty or clean. But none solves our issue.This work also aims to integrate with Crowbar and to contribute to its possible commercialization.
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Freitas, Paulo André Faria de. "Software Repository Mining Analytics to Estimate Software Component Reliability." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/83489.

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Encontrar e corrigir bugs em Software tem um grande custo e impacto no esforço em desenvolver Software. Os repositórios escondem informação preditiva sobre o histórico de Software que pode ser explorada recorrendo a técnicas de análise e de machine learning. A investigação atual de Mining Software Repositories (MSR) é capaz de classificar e listar componentes defeituosos com a granularidade ao nível do ficheiro. Os nossos objetivos são prever defeitos em Software com a granularidade até ao método, ao minar repositórios, e melhorar as técnicas de localização de falhas atuais com os resultados da previsão de defeitos. Foi implementada uma ferramenta denominada de Schwa, disponível livremente no Github, que é capaz de analisar repositórios Git. Para Java e outras linguagens conseguimos chegar à granularidade ao nível do método e ficheiro, respectivamente. Métricas como as revisões, correcções de bugs e autores são analisadas e usadas para alimentar o modelo de previsão com relevância temporal. Esta tese faz as seguintes contribuições: um método para interpretar e representar diffs de patches com a granularidade ao método; um modelo para calcular probabilidades de defeito; uma framework para minar repositórios de Software; uma técnica para aprender a importância das métricas analisadas; um método para avaliar o ganho de usar as probabilidade de defeito em localização de falhas.
Finding and fixing software bugs is expensive and has a significant impact in Software development effort. Repositories have hidden predictive information about Software history that can be explored using analytics and machine learning techniques. Current research in Mining Software Repositories (MSR) is capable of ranking and listing faulty components at the file granularity. Our goals are predicting Software defects with method granularity, by mining repositories, and improve current fault localization techniques with the results from defect prediction. We have implemented a tool called Schwa, available for free on Github, that is capable of analyzing Git repositories. For Java and others languages we achieved method and file granularity, respectively. Metrics such as revisions, fixes and authors are tracked and used to feed the prediction model with time relevance. This thesis does the following contributions: a method to parse and represent diffs from patches with method granularity; a model to compute defect probabilities; a framework for mining Software repositories; a technique to learn the importance of tracked metrics; a method to evaluate the gain of using defect probabilities in fault localization.
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Duarte, André Tiago Oliveira da Silva. "Software Repository Mining Analytics to Estimate Software Component Reliability." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/89450.

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Dada a crescente necessidade de identificar a localização dos erros no código fonte de software, de forma a facilitar o trabalho dos programadores e a acelerar o processo de desenvolvimento, muitos avanços têm sido feitos na sua automação.Existem três abordagens principais: Program-spectra based (PSB), Model-based diagnosis (MDB) e Program slicing.Barinel, solução que integra tanto o PSB como o MDB, é, até hoje, com base na investigação feita, a que apresenta melhores resultados. Contudo, a ordenação de conjuntos de candidatos (componentes faltosos) não tem em conta a verdadeira qualidade do componente em causa, mas sim o conjunto de valores que maximizam a probabilidade do conjunto (Maximum Likehood Estimation - MLE), devido à dificuldade da sua determinação.Com esta tese pretende-se colmatar esta falha e contribuir para uma melhor ordenação dos conjuntos, classificando, com recurso a técnicas de Machine Learning como Naive Bayes, Support Vector Machines (SVM) ou Random Forests, a qualidade e fiabilidade de cada componente, através das informações disponíveis no sistema de controlo de versões (Software Repository Mining), neste caso Git, como por exemplo: número de vezes que foi modificado, número de contribuidores, data de última alteração, nome de últimos contribuidores e tamanho das alterações.A investigação já feita, revelou a existência de algumas soluções de análise preditiva de software, como BugCache, FixCache e Change Classification, capazes de identificar componentes com grande probabilidade de falhar e de classificar as revisões (commits) como faltosas ou não, mas nenhuma soluciona o problema.Este trabalho visa também a integração com o Crowbar e a contribuição para a sua possível comercialização.
Given the rising necessity of identifying errors on the source code of software, in order to make the developers work easier and to speed up the development process, many progresses have been made in its automation.There are three main approaches: Program-spectra based (PSB), Model-based diagnosis (MDB) and Program slicing.Barinel, solution that integrates both PSB and MDB, is, until now, to our knowledge, the option that guarantees the best results. Despite this, the candidates (faulty components) set order doesn't take into account the real quality of the given component. With this thesis we want to fix this issue and contribute for a better candidates ordered set, classifying the quality and reliability of each component, using Machine Learning techniques such as Decision Trees, Support Vector Machines or Random Forests with the information extracted from Git, like: number of times it was modified, number of contributors, date of last change and size of those changes.The research revealed the existence of some software predictive analysis solutions, such as BugCache, FixCache and Change Classification, capable of identifying the components with a high probability of failure and of classifying the changes (commits) as faulty or clean. But none solves our issue.This work also aims to integrate with Crowbar and to contribute to its possible commercialization.
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Freitas, Paulo André Faria de. "Software Repository Mining Analytics to Estimate Software Component Reliability." Master's thesis, 2015. https://repositorio-aberto.up.pt/handle/10216/83489.

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Encontrar e corrigir bugs em Software tem um grande custo e impacto no esforço em desenvolver Software. Os repositórios escondem informação preditiva sobre o histórico de Software que pode ser explorada recorrendo a técnicas de análise e de machine learning. A investigação atual de Mining Software Repositories (MSR) é capaz de classificar e listar componentes defeituosos com a granularidade ao nível do ficheiro. Os nossos objetivos são prever defeitos em Software com a granularidade até ao método, ao minar repositórios, e melhorar as técnicas de localização de falhas atuais com os resultados da previsão de defeitos. Foi implementada uma ferramenta denominada de Schwa, disponível livremente no Github, que é capaz de analisar repositórios Git. Para Java e outras linguagens conseguimos chegar à granularidade ao nível do método e ficheiro, respectivamente. Métricas como as revisões, correcções de bugs e autores são analisadas e usadas para alimentar o modelo de previsão com relevância temporal. Esta tese faz as seguintes contribuições: um método para interpretar e representar diffs de patches com a granularidade ao método; um modelo para calcular probabilidades de defeito; uma framework para minar repositórios de Software; uma técnica para aprender a importância das métricas analisadas; um método para avaliar o ganho de usar as probabilidade de defeito em localização de falhas.
Finding and fixing software bugs is expensive and has a significant impact in Software development effort. Repositories have hidden predictive information about Software history that can be explored using analytics and machine learning techniques. Current research in Mining Software Repositories (MSR) is capable of ranking and listing faulty components at the file granularity. Our goals are predicting Software defects with method granularity, by mining repositories, and improve current fault localization techniques with the results from defect prediction. We have implemented a tool called Schwa, available for free on Github, that is capable of analyzing Git repositories. For Java and others languages we achieved method and file granularity, respectively. Metrics such as revisions, fixes and authors are tracked and used to feed the prediction model with time relevance. This thesis does the following contributions: a method to parse and represent diffs from patches with method granularity; a model to compute defect probabilities; a framework for mining Software repositories; a technique to learn the importance of tracked metrics; a method to evaluate the gain of using defect probabilities in fault localization.
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Books on the topic "Component software – Reliability"

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Christine, Hofmeister, Crnkovic Ivica, and Reussner Ralf, eds. Quality of software architectures: Second International Conference on Quality of Software Architectures, QoSA 2006, Västeraas, Sweden, June 27-29, 2006 : revised papers. Berlin: Springer, 2006.

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International Conference on the Quality of Software Architectures (6th 2010 Prague, Czech Republic). Research into practice - reality and gaps: 6th International Conference on the Quality of Software Architectures, QoSA 2010, Prague, Czech Republic, June 23-25, 2010 ; proceedings. Berlin: Springer, 2010.

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David, Hutchison. Architectures for Adaptive Software Systems: 5th International Conference on the Quality of Software Architectures, QoSA 2009, East Stroudsburg, PA, USA, June 24-26, 2009 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Safety-critical Systems Symposium (10th 2002 Southampton, England). Components of system safety: Proceedings of the Tenth Safety-critical Systems Symposium, Southampton, UK, 2002. London: Springer, 2002.

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Sickle, Ted Van. Reusable software components: Object-oriented embedded systems programming in C. Upper Saddle River, N.J: Prentice Hall PTR, 1997.

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Ivica, Crnkovic, and Larsson Magnus 1969-, eds. Building reliable component-based software systems. Boston: Artech House, 2002.

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(Editor), Ivica Crnkovic, and Magnus Larsson (Editor), eds. Building Reliable Component-Based Software Systems. Artech House Publishers, 2002.

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(Editor), Christine Hofmeister, Ivica Crnkovic (Editor), and Ralf Reussner (Editor), eds. Quality of Software Architectures: Second International Conference on Quality of Software Architectures, QoSA 2006, Västeras, Schweden, June 27-29, 2006, ... Papers (Lecture Notes in Computer Science). Springer, 2007.

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Components of System Safety: Proceedings of the Tenth Safety-critical Systems Symposium, Southampton, UK, 2002. Springer, 2002.

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Reusable Software Components: Object-Oriented Embedded Systems Programming in C (Prentice Hall Series on Programming Tools and Methodologies). Prentice Hall, 1996.

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Book chapters on the topic "Component software – Reliability"

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McGregor, John D., Judith A. Stafford, and Il-Hyung Cho. "Measuring and Communicating Component Reliability." In Software Engineering Research and Applications, 74–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24675-6_9.

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Shukla, Rakesh, Paul Strooper, and David Carrington. "A Framework for Reliability Assessment of Software Components." In Component-Based Software Engineering, 272–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24774-6_24.

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Filieri, Antonio, Carlo Ghezzi, Vincenzo Grassi, and Raffaela Mirandola. "Reliability Analysis of Component-Based Systems with Multiple Failure Modes." In Component-Based Software Engineering, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13238-4_1.

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Kim, Hye Yeon, Kshamta Jerath, and Frederick Sheldon. "Assessment of High Integrity Software Components for Completeness, Consistency, Fault-Tolerance, and Reliability." In Component-Based Software Quality, 259–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45064-1_13.

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Grassi, Vincenzo, Raffaela Mirandola, and Antonino Sabetta. "A Model Transformation Approach for the Early Performance and Reliability Analysis of Component-Based Systems." In Component-Based Software Engineering, 270–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11783565_19.

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Brosch, Franz, Heiko Koziolek, Barbora Buhnova, and Ralf Reussner. "Parameterized Reliability Prediction for Component-Based Software Architectures." In Research into Practice – Reality and Gaps, 36–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13821-8_5.

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Rodrigues, Genaína, David Rosenblum, and Sebastian Uchitel. "Using Scenarios to Predict the Reliability of Concurrent Component-Based Software Systems." In Fundamental Approaches to Software Engineering, 111–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31984-9_9.

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Jasra, Bhat, and Sanjay Kumar Dubey. "Reliability Assessment of Component-Based Software System Using Fuzzy-AHP." In Advances in Intelligent Systems and Computing, 663–70. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8848-3_64.

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Yamada, Shigeru, and Yoshinobu Tamura. "Component-Oriented Reliability Assessment Approach Based on Decision-Making Frameworks for Open Source Software." In Springer Series in Reliability Engineering, 587–608. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30599-8_23.

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Dimov, Aleksandar, and Sasikumar Punnekkat. "On the Estimation of Software Reliability of Component-Based Dependable Distributed Systems." In Lecture Notes in Computer Science, 171–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11558569_13.

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Conference papers on the topic "Component software – Reliability"

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Cheung, Leslie, Roshanak Roshandel, Nenad Medvidovic, and Leana Golubchik. "Early prediction of software component reliability." In the 13th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1368088.1368104.

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Dong, Wang, Huang Ning, and Ye Ming. "Reliability Analysis of Component-Based Software Based on Relationships of Components." In 2008 IEEE International Conference on Web Services (ICWS). IEEE, 2008. http://dx.doi.org/10.1109/icws.2008.83.

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Mohan, Amitha, and Shambhu Kumar Jha. "Predicting and Accessing Reliability of Components in Component Based Software Development." In 2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019. http://dx.doi.org/10.1109/iccs45141.2019.9065290.

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Xie, Qishun, Jun Bai, Ce Zhang, Fanchao Meng, Gang Cui, and Hongwei Liu. "A Component-Based Software Reliability Growth Model Considering Differences in Components." In 2013 International Conference on Software Engineering and Computer Science. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icsecs-13.2013.8.

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Ling, Dongyi, Bin Liu, and Shihai Wang. "A component-based software reliability assessment method considering component effective behavior." In 2017 Second International Conference on Reliability Systems Engineering (ICRSE). IEEE, 2017. http://dx.doi.org/10.1109/icrse.2017.8030748.

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Roshandel, Roshanak, Somo Banerjee, Leslie Cheung, Nenad Medvidovic, and Leana Golubchik. "Estimating software component reliability by leveraging architectural models." In Proceeding of the 28th international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1134285.1134432.

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Mao Xiaoguang and Deng Yongjin. "A general model for component-based software reliability." In Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat No 03CH37412) EURMIC-03. IEEE, 2003. http://dx.doi.org/10.1109/eurmic.2003.1231620.

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Dimov, Aleksandar, and Sasikumar Punnekkat. "Fuzzy Reliability Model for Component-Based Software Systems." In 2010 36th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2010. http://dx.doi.org/10.1109/seaa.2010.46.

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Ren, Wenna, and Baolong Guo. "Research on Reliability Analysis of Component-Based Software." In 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/iceea-18.2018.50.

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Shin, Michael E., Snehadeep Sethia, and Nipul Patel. "Component-Based Malicious Software Engineer Intrusion Detection." In 2011 International Conference on Secure Software Integration and Reliability Improvement (SSIRI). IEEE, 2011. http://dx.doi.org/10.1109/ssiri.2011.33.

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Journal articles
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