Java se 7 programmer i 1z0 803 pdf

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You can choose your language settings from within the program. Version based development of software projects are considered to design an iterative prediction approach. Proposed methodology is developed as Eclipse plugin. Experiments show that proposed methodology gives promising results to use SFP in daily routine of software development phases. In this study, we consider a software fault prediction task that can assist a developer during the lifetime of a project.

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We aim to improve the performance of software fault prediction task while keeping it as applicable. In this paper, an Artificial Neural Network and Adaptive Neuro Fuzzy Inference System are employed. We propose an iterative prediction model that begins with a FIS when no data are available for the software project and continues with a data-driven method when adequate data become available. To prove the usability of this iterative prediction approach, software fault prediction experiments are performed using expert knowledge for the initial version and information about previous versions for subsequent versions. The metrics of the models are common object-oriented metrics, such as coupling between objects, weighted methods per class and response for a class. The results of the models are evaluated according to the receiver operating characteristics with the area under the curve approach. The results indicate that the iterative software fault prediction is successful and can be transformed into a tool that can automatically locate fault-prone modules due to its well-organized information flow.