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One kind of empirical data on the workload of a software bug fixes Prediction Model

Author: DingChunLong
Tutor: LuYanSheng
School: Huazhong University of Science and Technology
Course: Computer Software and Theory
Keywords: Defect prediction Text Classification Data Mining Support Vector Machine
CLC: TP311.53
Type: Master's thesis
Year: 2011
Downloads: 30
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Software defect repair workload forecasting refers to the actual repair work before beginning repair of defects required to spend a certain amount of work to predict . To study the issue of great significance : as software development and maintenance process of resource allocation decision support ; reduce software development process unpredictable and improve the software process automation ; beneficial software quality assurance and so on. However, due to a software bug fixes workload forecasting problems inherent complexity and particularity , the problem has not been solved . Existing defect repair workload forecasting models typically use non-text information to predict widespread application of high cost, low prediction accuracy can not be in the early stages of the life cycle defect prediction and can not be cross-project forecasts and other shortcomings, yet away from the practical greater distance . Therefore , the need for software defect repair workload forecasting problems for a more in-depth study . TCEPM is a text-based classification software bug fixes workload prediction model, the model assumes that similar defects have similar descriptive text , takes a similar repair workload. TCEPM model by using the SVM algorithm to classify the defect described in the text , to predict the effects of workload bug fixes . TCEPM model prediction process mainly consists of three steps: 1 ) use of Chinese word segmentation, feature extraction techniques text description of the defect text preprocessing , the defect description text into feature vectors ; 2 ) using SVM classification algorithm and experiences defect data , prediction model for training ; 3 ) using a trained model, the newly submitted defect repair work needed to predict . Experimental results show that , TCEPM model can achieve high prediction accuracy is better than predicted using non- textual information predictive models. Furthermore , TCEPM model also has a life cycle of the defect can be predicted early stage , the application cost is small, can be predicted across projects , etc. , to overcome the deficiencies of the existing prediction model , has a high practical value.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Software Engineering > Software Maintenance
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