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Towards a self-evolving software defect detection processYang, Ximin 15 August 2007
Software defect detection research typically focuses on individual inspection and testing techniques. However, to be effective in applying defect detection techniques, it is important to recognize when to use inspection techniques and when to use testing techniques. In addition, it is important to know when to deliver a product and use maintenance activities, such as trouble shooting and bug fixing, to address the remaining defects in the software.<p>To be more effective detecting software defects, not only should defect detection techniques be studied and compared, but the entire software defect detection process should be studied to give us a better idea of how it can be conducted, controlled, evaluated and improved.<p>This thesis presents a self-evolving software defect detection process (SEDD) that provides a systematic approach to software defect detection and guides us as to when inspection, testing or maintenance activities are best performed. The approach is self-evolving in that it is continuously improved by assessing the outcome of the defect detection techniques in comparison with historical data.<p>A software architecture and prototype implementation of the approach is also presented along with a case study that was conducted to validate the approach. Initial results of using the self-evolving defect detection approach are promising.
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Towards a self-evolving software defect detection processYang, Ximin 15 August 2007 (has links)
Software defect detection research typically focuses on individual inspection and testing techniques. However, to be effective in applying defect detection techniques, it is important to recognize when to use inspection techniques and when to use testing techniques. In addition, it is important to know when to deliver a product and use maintenance activities, such as trouble shooting and bug fixing, to address the remaining defects in the software.<p>To be more effective detecting software defects, not only should defect detection techniques be studied and compared, but the entire software defect detection process should be studied to give us a better idea of how it can be conducted, controlled, evaluated and improved.<p>This thesis presents a self-evolving software defect detection process (SEDD) that provides a systematic approach to software defect detection and guides us as to when inspection, testing or maintenance activities are best performed. The approach is self-evolving in that it is continuously improved by assessing the outcome of the defect detection techniques in comparison with historical data.<p>A software architecture and prototype implementation of the approach is also presented along with a case study that was conducted to validate the approach. Initial results of using the self-evolving defect detection approach are promising.
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Abordagem semi-supervisionada para detecção de módulos de software defeituososOLIVEIRA, Paulo César de 31 August 2015 (has links)
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Previous issue date: 2015-08-31 / Com a competitividade cada vez maior do mercado, aplicações de alto nível de
qualidade são exigidas para a automação de um serviço. Para garantir qualidade de
um software, testá-lo visando encontrar falhas antecipadamente é essencial no ciclo
de vida de desenvolvimento. O objetivo do teste de software é encontrar falhas que
poderão ser corrigidas e consequentemente, aumentar a qualidade do software em
desenvolvimento. À medida que o software cresce, uma quantidade maior de testes
é necessária para prevenir ou encontrar defeitos, visando o aumento da qualidade.
Porém, quanto mais testes são criados e executados, mais recursos humanos e de
infraestrutura são necessários. Além disso, o tempo para realizar as atividades de
teste geralmente não é suficiente, fazendo com que os defeitos possam escapar.
Cada vez mais as empresas buscam maneiras mais baratas e efetivas para detectar
defeitos em software. Muitos pesquisadores têm buscado nos últimos anos,
mecanismos para prever automaticamente defeitos em software. Técnicas de
aprendizagem de máquina vêm sendo alvo das pesquisas, como uma forma de
encontrar defeitos em módulos de software. Tem-se utilizado muitas abordagens
supervisionadas para este fim, porém, rotular módulos de software como defeituosos
ou não para fins de treinamento de um classificador é uma atividade muito custosa e
que pode inviabilizar a utilização de aprendizagem de máquina. Neste contexto, este
trabalho propõe analisar e comparar abordagens não supervisionadas e semisupervisionadas
para detectar módulos de software defeituosos. Para isto, foram
utilizados métodos não supervisionados (de detecção de anomalias) e também
métodos semi-supervisionados, tendo como base os classificadores AutoMLP e
Naive Bayes. Para avaliar e comparar tais métodos, foram utilizadas bases de dados
da NASA disponíveis no PROMISE Software Engineering Repository. / Because the increase of market competition then high level of quality applications
are required to provide automate services. In order to achieve software quality testing
is essential in the development lifecycle with the purpose of finding defect as earlier
as possible. The testing purpose is not only to find failures that can be fixed, but
improve software correctness and quality. Once software gets more complex, a
greater number of tests will be necessary to prevent or find defects. Therefore, the
more tests are designed and exercised, the more human and infrastructure
resources are needed. However, time to run the testing activities are not enough,
thus, as a result, it causes escape defects. Companies are constantly trying to find
cheaper and effective ways to software defect detection in earlier stages. In the past
years, many researchers are trying to finding mechanisms to automatically predict
these software defects. Machine learning techniques are being a research target, as
a way of finding software modules detection. Many supervised approaches are being
used with this purpose, but labeling software modules as defective or not defective to
be used in training phase is very expensive and it can make difficult machine learning
use. Considering that this work aims to analyze and compare unsupervised and
semi-supervised approaches to software module defect detection. To do so,
unsupervised methods (of anomaly detection) and semi-supervised methods using
AutoMLP and Naive Bayes algorithms were used. To evaluate and compare these
approaches, NASA datasets were used at PROMISE Software Engineering
Repository.
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