Cover Image

Techniques to facilitate probabilistic software analysis in real-world programs

Probabilistic software analysis aims at quantifying how likely a target event is to occur, given a probabilistic characterization of the behavior of a program or of its execution environment. Examples of target events may include an uncaught exception, the invocation of a certain method, or the a...

Full description

Main Author: BORGES, Mateus Araújo
Other Authors: D'AMORIM, Marcelo Bezerra
Format: masterThesis
Language: por
Published: Universidade Federal de Pernambuco 2016
Subjects:
Execução Simbólica
Amostragem de Monte Carlo
Análise Probabilística
Testes
Online Access: https://repositorio.ufpe.br/handle/123456789/14932
Tags: Add Tag
No Tags, Be the first to tag this record!
id ir-123456789-14932
recordtype dspace
spelling ir-123456789-149322019-10-26T00:24:03Z Techniques to facilitate probabilistic software analysis in real-world programs BORGES, Mateus Araújo D'AMORIM, Marcelo Bezerra http://lattes.cnpq.br/9394743532378351 http://lattes.cnpq.br/3762670242328435 Execução Simbólica Amostragem de Monte Carlo Análise Probabilística Testes Probabilistic software analysis aims at quantifying how likely a target event is to occur, given a probabilistic characterization of the behavior of a program or of its execution environment. Examples of target events may include an uncaught exception, the invocation of a certain method, or the access to confidential information. The technique collects constraints on the inputs that lead to the target events and analyzes them to quantify how likely it is for an input to satisfy the constraints. Current techniques either handle only linear constraints or only support continuous distributions using a “discretization” of the input domain, leading to imprecise and costly results. This work proposes an iterative distribution-aware sampling approach to support probabilistic symbolic execution for arbitrarily complex mathematical constraints and continuous input distributions. We follow a compositional approach, where the symbolic constraints are decomposed into sub-problems whose solution can be solved independently. At each iteration the convergence rate of the computation is increased by automatically refocusing the analysis on estimating the sub-problems that mostly affect the accuracy of the results, as guided by three different ranking strategies. Experiments on publicly available benchmarks show that the proposed technique improves on previous approaches in terms of scalability and accuracy of the results. FACEPE Análise Probabilística de Software (PSA) visa a quantificar a probabilidade de que um evento de interesse seja alcançado durante a execução de um programa, dada uma caracterização probabilística do comportamento do programa ou do seu ambiente de execução. O evento de interesse pode ser, por exemplo, uma exceção não capturada, a invocação de um método específico, ou o acesso à informação confidencial. A técnica coleta restrições sobre as entradas que levam para os eventos de interesse e as analisa para quantificar o quão provável que uma entrada satisfaça essas restrições. Técnicas atuais ou suportam apenas restrições lineares, ou suportam distribuições contínuas utilizando uma "discretização" do domínio de entrada, levando a resultados imprecisos e caros. Este trabalho apresenta uma abordagem iterativa, composicional e sensível às distribuições para suportar o uso de PSA em restrições com operações matemáticas arbitrariamente complexas e distribuições contínuas de entrada. Nossa abordagem composicional permite que as restrições sejam decompostas em subproblemas que podem ser resolvidos independentemente. Em cada iteração a análise é reorientada automaticamente para a estimação dos subproblemas que mais afetam a precisão dos resultados, assim aumentando a taxa de convergência da computação. Esta reorientação é guiada por três diferentes estratégias de ranqueamento. Experimentos em programas publicamente disponíveis mostram que a técnica proposta é melhor do que abordagens existentes em termos de escalabilidade e precisão. 2016-01-19T17:42:17Z 2016-01-19T17:42:17Z 2015-04-24 masterThesis https://repositorio.ufpe.br/handle/123456789/14932 por Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ application/pdf Universidade Federal de Pernambuco UFPE Brasil Programa de Pos Graduacao em Ciencia da Computacao
institution REPOSITORIO UFPE
collection REPOSITORIO UFPE
language por
topic Execução Simbólica
Amostragem de Monte Carlo
Análise Probabilística
Testes
spellingShingle Execução Simbólica
Amostragem de Monte Carlo
Análise Probabilística
Testes
BORGES, Mateus Araújo
Techniques to facilitate probabilistic software analysis in real-world programs
description Probabilistic software analysis aims at quantifying how likely a target event is to occur, given a probabilistic characterization of the behavior of a program or of its execution environment. Examples of target events may include an uncaught exception, the invocation of a certain method, or the access to confidential information. The technique collects constraints on the inputs that lead to the target events and analyzes them to quantify how likely it is for an input to satisfy the constraints. Current techniques either handle only linear constraints or only support continuous distributions using a “discretization” of the input domain, leading to imprecise and costly results. This work proposes an iterative distribution-aware sampling approach to support probabilistic symbolic execution for arbitrarily complex mathematical constraints and continuous input distributions. We follow a compositional approach, where the symbolic constraints are decomposed into sub-problems whose solution can be solved independently. At each iteration the convergence rate of the computation is increased by automatically refocusing the analysis on estimating the sub-problems that mostly affect the accuracy of the results, as guided by three different ranking strategies. Experiments on publicly available benchmarks show that the proposed technique improves on previous approaches in terms of scalability and accuracy of the results.
author2 D'AMORIM, Marcelo Bezerra
format masterThesis
author BORGES, Mateus Araújo
author_sort BORGES, Mateus Araújo
title Techniques to facilitate probabilistic software analysis in real-world programs
title_short Techniques to facilitate probabilistic software analysis in real-world programs
title_full Techniques to facilitate probabilistic software analysis in real-world programs
title_fullStr Techniques to facilitate probabilistic software analysis in real-world programs
title_full_unstemmed Techniques to facilitate probabilistic software analysis in real-world programs
title_sort techniques to facilitate probabilistic software analysis in real-world programs
publisher Universidade Federal de Pernambuco
publishDate 2016
url https://repositorio.ufpe.br/handle/123456789/14932
_version_ 1648654697217654784
score 13.657419

Similar Items