1,091 research outputs found

    Software Defect Association Mining and Defect Correction Effort Prediction

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    Much current software defect prediction work concentrates on the number of defects remaining in software system. In this paper, we present association rule mining based methods to predict defect associations and defect-correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that for the defect association prediction, the accuracy is very high and the false negative rate is very low. Likewise for the defect-correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect-correction effort prediction method with other types of methods: PART, C4.5, and Na¨ıve Bayes and show that accuracy has been improved by at least 23%. We also evaluated the impact of support and confidence levels on prediction accuracy, false negative rate, false positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy

    Proceedings of the Twenty-Third Annual Software Engineering Workshop

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    The Twenty-third Annual Software Engineering Workshop (SEW) provided 20 presentations designed to further the goals of the Software Engineering Laboratory (SEL) of the NASA-GSFC. The presentations were selected on their creativity. The sessions which were held on 2-3 of December 1998, centered on the SEL, Experimentation, Inspections, Fault Prediction, Verification and Validation, and Embedded Systems and Safety-Critical Systems

    A FRAMEWORK FOR SOFTWARE RELIABILITY MANAGEMENT BASED ON THE SOFTWARE DEVELOPMENT PROFILE MODEL

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    Recent empirical studies of software have shown a strong correlation between change history of files and their fault-proneness. Statistical data analysis techniques, such as regression analysis, have been applied to validate this finding. While these regression-based models show a correlation between selected software attributes and defect-proneness, in most cases, they are inadequate in terms of demonstrating causality. For this reason, we introduce the Software Development Profile Model (SDPM) as a causal model for identifying defect-prone software artifacts based on their change history and software development activities. The SDPM is based on the assumption that human error during software development is the sole cause for defects leading to software failures. The SDPM assumes that when a software construct is touched, it has a chance to become defective. Software development activities such as inspection, testing, and rework further affect the remaining number of software defects. Under this assumption, the SDPM estimates the defect content of software artifacts based on software change history and software development activities. SDPM is an improvement over existing defect estimation models because it not only uses evidence from current project to estimate defect content, it also allows software managers to manage software projects quantitatively by making risk informed decisions early in software development life cycle. We apply the SDPM in several real life software development projects, showing how it is used and analyzing its accuracy in predicting defect-prone files and compare the results with the Poisson regression model

    Estimando completitud en Ingeniería de Requisitos

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    La Ingeniería de Requisitos provee mecanismos para elicitar y especificar requisitos, procurando maximizar calidad y completitud. Sin embargo, estimar el grado de completitud de los requisitos de un sistema de software es muy difícil. El mismo problema se presenta en diversas áreas del proceso de desarrollo de software. La introducción de técnicas de predicción basadas en modelos estadísticos lleva ya varios años en el campo de la Ingeniería de Software, con muy buenos resultados. Este proyecto pretende estudiar la completitud de los requisitos de un proyecto de software, analizando la completitud de cada uno de los modelos utilizados en el proceso de obtención de los requisitos, y el impacto que la completitud de cada modelo tiene sobre el resto.Eje: Ingeniería de software y base de datosRed de Universidades con Carreras en Informática (RedUNCI

    Estimando completitud en Ingeniería de Requisitos

    Get PDF
    La Ingeniería de Requisitos provee mecanismos para elicitar y especificar requisitos, procurando maximizar calidad y completitud. Sin embargo, estimar el grado de completitud de los requisitos de un sistema de software es muy difícil. El mismo problema se presenta en diversas áreas del proceso de desarrollo de software. La introducción de técnicas de predicción basadas en modelos estadísticos lleva ya varios años en el campo de la Ingeniería de Software, con muy buenos resultados. Este proyecto pretende estudiar la completitud de los requisitos de un proyecto de software, analizando la completitud de cada uno de los modelos utilizados en el proceso de obtención de los requisitos, y el impacto que la completitud de cada modelo tiene sobre el resto.Eje: Ingeniería de software y base de datosRed de Universidades con Carreras en Informática (RedUNCI
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