Software project risk assessment method and device
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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no. 1 approach
[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 apparatus 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 about 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 into the database 201 .
[0038] As an example of saving in the database 201, a table may be used to save project risk records and historical project indicators. Figure 5 is an illustration of the project risk record and historical ...
no. 2 approach
[0082] In the first embodiment, it is assumed that all project indicators are project indicators related to risk to establish a risk prediction model. However, since all item indicators are related item indicators, the impact of item types on item indicators is ignored, and item indicators with high correlation degree are mixed with item indicators with low correlation degree, which increases the calculation amount of model training, and in To some 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 degree of correlation, thereby reducing the amount of calculation for model training and improving the accuracy of the prediction model. Spend. This second embodiment is especially effective when the sample size is large.
[0083] The configuration of the main body of the device in the second embodiment an...
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