F. Veri Madenciliğinin Kullanıldığı Bir Sahtekârlık Örneğinin İncelemesi
1. Örnek Olay Üzerinde Veri Madenciliği Süreci
A etapa de Teste de Software é tratada por diversos autores como a atividade de maior custo para o desenvolvimento do software, pois é nesta etapa que serão verificadas a qualidade e a eficiência do produto.
A preocupação em reduzir os custos sem comprometer a qualidade do software fez com que outra área da Ciência da Computação e da Matemática Aplicada fosse então inserida nesta etapa do Teste de Software para resolver este problema, que são as técnicas de otimização.
A união dessas duas áreas foi chamada por Harman e Jones em 2001 de
Search Based Software Engineering (SBSE). Apesar de este termo ter sido proposto
apenas em 2001, observa-se que, em meados da década de 70, já apareciam os primeiros trabalhos de pesquisa (MILLER e SPOONER, 1976) utilizando a técnica de otimização em problemas de Teste de Software. Entre 1976 e 2000, eram apenas 60 artigos publicados, enquanto entre 2001 e 2014, já foram contabilizadas 652 publicações, mostrando o grande despertar da comunidade acadêmica para esta nova área de pesquisa.
Uma classificação dos trabalhos publicados no período de 2009 a 2014 considerou os algoritmos de otimização utilizados pelos autores e as suas principais características. Importante ressaltar que, na grande maioria dos trabalhos, os autores comparam os experimentos a outras técnicas/algoritmos e não buscam estender trabalhos já existentes.
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Anexo A: Algoritmos de Otimização
Neste apêndice, serão descritos os algoritmos de otimização utilizados por diversos pesquisadores na área de Teste de Software para o problema de geração de dados de teste.
A.1. Algoritmo Genético
O Algoritmo Genético (AG) é um exemplo de Algoritmo Evolutivo que trata de uma técnica de busca heurística (MARAGATHAVALLI, 2011). São conhecidos como algoritmo de busca global, uma vez que a amostragem de muitos pontos no espaço de busca de uma só vez oferece maior resistência aos mínimos locais. Normalmente, um AG utiliza uma representação binária, ou seja, as soluções candidatas são codificadas como sequências de 1s e 0s; contudo, também pode ser utilizada representação mais natural para o problema, como, por exemplo, uma lista de valores de ponto flutuante (HARMAN et al., 2012).
Proposto por John Holland em 1975, o AG é inspirado na evolução Darwiniana e no conceito de sobrevivência do “mais apto”. Cada ponto no espaço de busca atual sob consideração é referido como um “indivíduo” ou um “cromossomo”. O atual conjunto de indivíduos atualmente sob consideração é coletivamente referido como “população” atual, e cada sucessiva população é chamada de geração. O loop principal do AG pode ser visto na Figura 8.
Figura 8: Visão geral das principais etapas do AG
A.2. Algoritmo Memético
O Algoritmo Memético é uma forma híbrida do Algoritmo Genético que utiliza algum método de Busca Local (descrito na seção 4.2). A maneira mais comum de implementar esse algoritmo é a utilização de um algoritmo de busca local simples que melhore a aptidão da população inicial e dos indivíduos resultantes da operação de recombinação. De forma geral, o Algoritmo Memético tem uma melhor performance do que os Algoritmo Genético (WHITLEY, 1994).
A.3. Estratégia Evolutiva
O algoritmo de Estratégia Evolutiva é mais simples do que o Algoritmo Genético. Este algoritmo se caracteriza pelo tamanho da população, que é igual a um, ou seja, existe apenas um indivíduo na população e ele é representado por uma sequência de bits. A exploração do espaço de busca é feita pelo operador de mutação bit a bit. Para produzir uma prole, este operador gera cada bit, independentemente, na cadeia de bits, com uma probabilidade (p) com base no comprimento da string. Caso a aptidão do filho seja melhor do que a do pai, o filho é mantido para a próxima geração (ARCURI, 2012).
A.4. Busca Aleatória
Busca Aleatória é o algoritmo de busca mais simples. Soluções aleatórias são verificadas até que um ótimo global seja encontrado ou um critério de parada seja atendido (tempo máximo, número máximo de tentativas etc.). A função de aptidão é responsável por informar se um ótimo global foi localizado. Busca Aleatória é geralmente utilizado na literatura como uma linha de base para comparação com outros algoritmos de busca. Uma característica importante deste algoritmo é a
distribuição de probabilidades utilizadas para verificação das novas soluções