Method and device for evaluating software project risks
A technology for risk assessment and software projects, applied in the field of software project risk assessment, can solve problems such as limited scope of application, low accuracy of quantitative assessment methods, difficult progress indicators, etc., to achieve a wide range of applications and reduce dependence on expert experience , the effect of expanding the scope of application
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[0035] (First embodiment)
[0036] figure 2 It is a block diagram of the software project risk assessment device according to the first embodiment. Such as figure 2 As shown, the software project risk assessment device of the present invention includes a collection unit 200, a segmentation unit 201, a statistics unit 203, and a calculation unit 204.
[0037] Wherein, the collection unit 200 collects historical data on project indicators and project risks of multiple samples into the database 201 for storage. For example, the collection unit 200 accepts user input through an input device or directly imports historical data from an external device to automatically / manually collect historical data of software projects and ongoing new project data, and save the data in the database 201.
[0038] As an example of storage in the database 201, a table can be used to store project risk records and historical project indicators. Figure 5 It is an example diagram of project risk records an...
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[0081] (Second embodiment)
[0082] In the first embodiment, it is assumed that all project indicators are risk-related project indicators to establish a risk prediction model. However, since all project indicators are set as relevant project indicators, the impact of project types on project indicators is ignored, and project indicators with high correlation are mixed with project indicators with low correlations, which increases the amount of calculation for model training. To a certain extent, it is possible to reduce the accuracy of the prediction model. Therefore, in the second embodiment, the process of pre-processing the collected data is added, and the types of project indicators are screened based on the correlation degree, thereby reducing the calculation amount of model training and improving the accuracy of the prediction model degree. This second embodiment is particularly effective when the sample range is large.
[0083] The configuration of the main body of the d...
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