The program is a complex object consisting of different units with variable degrees of defects. By predicting the effectiveness and frequency of program defects, program managers can make better use of manpower, cost, and time to obtain better quality assurance. It is always possible to have a set of defects that affect designed and predictable units in order to have close association with the subsidiaries. Most of the current defect prediction rating mechanism is derived from learning the previous project data, but it is not sufficient to predict the defect of the new project because the new design may contain a different type of parameter. This paper proposes a Software Defect Learning and Analysis utilizing Regression Method (SDLA-RM) to detect defects and plan a better maintenance strategy, which can support the prediction of a defective or nondefective software unit prior to deployment in any project programs. The SDL-RM mechanism extends Regression Analysis (RAM) to create an effective rulebased model for accurately classifying program faults. This approach improves the predictability of software defects, allowing software development to spend more time testing components that are expected to contain errors. The experimental evaluation is carried out across the NASA-PROMISE repository data sets, that outcome of the results in comparison with existing classifiers suggest the effectiveness and practical perspective in the software development.
While developing software it is very important that the software should be of defect free. But, none of the software can be 100% defect free and various studies are in progress to build a model which minimizes the defect as much as possible by predicting it at an early stage of development. Based on the probability facts various researchers has used probabilistic model to predict defects in the program. To contribute in this research and enhancing the existing model of software defect prediction we are proposing a model based on the combination of probabilistic and deterministic model through defect association learning. The experimental evaluation in comparison with the existing methods shows the improvement in the accuracy of predicting the defect by using Deterministic and Probabilistic defect prediction.
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