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The work described in this thesis is the result of aresearch program started in 1981 to find better ways ofprogramming Telecom applications. These applications are largeprograms which despite careful testing will probably containmany errors when the program is put into service. We assumethat such programs do contain errors, and investigate methodsfor building reliable systems despite such errors. The research has resulted in the development of a newprogramming language (called Erlang), together with a designmethodology, and set of libraries for building robust systems(called OTP). At the time of writing the technology describedhere is used in a number of major Ericsson, and Nortelproducts. A number of small companies have also been formedwhich exploit the technology. The central problem addressed by this thesis is the problemof constructing reliablesystems from programs which maythemselves contain errors. Constructing such systems imposes anumber of requirements on any programming language that is tobe used for the construction. I discuss these languagerequirements, and show how they are satisfied by Erlang. Problems can be solved in a programming language, or in thestandard libraries which accompany the language. I argue howcertain of the requirements necessary to build a fault-tolerantsystem are solved in the language, and others are solved in thestandard libraries. Together these form a basis for buildingfault-tolerant software systems. No theory is complete without proof that the ideas work inpractice. To demonstrate that these ideas work in practice Ipresent a number of case studies of large commerciallysuccessful products which use this technology. At the time ofwriting the largest of these projects is a major Ericssonproduct, having over a million lines of Erlang code. Thisproduct (the AXD301) is thought to be one of the most reliableproducts ever made by Ericsson. Finally, I ask if the goal of finding better ways to programTelecom applications was fulfilled --- I also point to areaswhere I think the system could be improved.

<p>The work described in this thesis is the result of aresearch program started in 1981 to find better ways ofprogramming Telecom applications. These applications are largeprograms which despite careful testing will probably containmany errors when the program is put into service. We assumethat such programs do contain errors, and investigate methodsfor building reliable systems despite such errors.</p><p>The research has resulted in the development of a newprogramming language (called Erlang), together with a designmethodology, and set of libraries for building robust systems(called OTP). At the time of writing the technology describedhere is used in a number of major Ericsson, and Nortelproducts. A number of small companies have also been formedwhich exploit the technology.</p><p>The central problem addressed by this thesis is the problemof constructing reliablesystems from programs which maythemselves contain errors. Constructing such systems imposes anumber of requirements on any programming language that is tobe used for the construction. I discuss these languagerequirements, and show how they are satisfied by Erlang.</p><p>Problems can be solved in a programming language, or in thestandard libraries which accompany the language. I argue howcertain of the requirements necessary to build a fault-tolerantsystem are solved in the language, and others are solved in thestandard libraries. Together these form a basis for buildingfault-tolerant software systems.</p><p>No theory is complete without proof that the ideas work inpractice. To demonstrate that these ideas work in practice Ipresent a number of case studies of large commerciallysuccessful products which use this technology. At the time ofwriting the largest of these projects is a major Ericssonproduct, having over a million lines of Erlang code. Thisproduct (the AXD301) is thought to be one of the most reliableproducts ever made by Ericsson.</p><p>Finally, I ask if the goal of finding better ways to programTelecom applications was fulfilled --- I also point to areaswhere I think the system could be improved.</p>

Made available in DSpace on 2016-06-02T20:06:10Z (GMT). No. of bitstreams: 1 6083.pdf: 1151427 bytes, checksum: 24c39bb02ef8c214a3e10c3cc5bae9ef (MD5) Previous issue date: 2014-03-14 / Financiadora de Estudos e Projetos / In this work, we consider the estimation of the number of errors in a software from a closed population. The process of estimating the population size is based on the capture-recapture method which consists of examining the software, in parallel, by a number of reviewers. The probabilistic model adopted accommodates situations in which reviewers are independent and homogeneous (equally efficient), and each error is an element that is part of a disjoint partition in relation to its detection probability. We propose an iterative process to obtain maximum likelihood estimates in which the EM algorithm is used to the nuisance parameters estimation. The estimates of population parameters were also obtained under the Bayesian approach, in which Monte Carlo on Markov Chains (MCMC) simulations through Gibbs sampling algorithm with insertion of latent variables were used on the conditional posterior distributions. The two approaches were applied to simulated data and in two real data sets from the literature. / Neste trabalho, consideramos a estimação do número de erros em um software provenientes de uma população fechada. O processo de estimação do tamanho populacional é baseado no método de captura-recaptura, que consiste em examinar o software, em paralelo, por certo número de revisores. O modelo probabilístico adotado acomoda situações em que os revisores são independentes e homogêneos (igualmente eficientes) e que cada erro é um elemento que faz parte de uma partição disjunta quanto à sua probabilidade de detecção. Propomos um processo iterativo para obtenção das estimativas de máxima verossimilhança em que utilizamos o algoritmo EM na estimação dos parâmetros perturbadores. As estimativas dos parâmetros populacionais também foram obtidas sob o enfoque Bayesiano, onde utilizamos simulações de Monte Carlo em Cadeias de Markov (MCMC) através do algoritmo Gibbs sampling com a inserção de variáveis latentes nas distribuições condicionais a posteriori. As duas abordagens foram aplicadas em dados simulados e em dois conjuntos de dados reais da literatura.