Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Method for predicting software workload of newly-added software project

A technology of software projects and forecasting methods, applied in the direction of program control devices, etc., can solve problems such as incompleteness of historical data

Inactive Publication Date: 2014-06-25
INST OF SOFTWARE - CHINESE ACAD OF SCI
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the difficulty of the incompleteness of historical data faced by software cost estimation, and the insufficiency of existing software cost estimation methods, the present invention proposes a software workload missing data repair and workload prediction method based on data mining (Missing Imputation Technique and Effort Prediction based on Data Mining, MITEP-DM)

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for predicting software workload of newly-added software project
  • Method for predicting software workload of newly-added software project
  • Method for predicting software workload of newly-added software project

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The following will specifically illustrate the data mining-based software workload missing data repair and prediction methods:

[0048] The MITEP-DM method proposes a new software project workload prediction method based on incomplete software workload historical data, and gives the final software workload prediction model, that is, the posterior probability distribution of software projects for workload categories Among them, P (θ) (c t ) is the prior probability of workload category in historical data; P (θ) (X j |c t ) is the conditional probability of the software item attribute relative to the workload category; x ij for the i-th item in the j-th attribute X j The above value. The specific implementation process is mainly divided into the following four steps:

[0049] (1) Collect data from historical projects:

[0050] Because the ultimate goal is to quantify the workload required for new software projects, only the workload features associated with the s...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for predicting the software workload of a newly-added software project, belonging to the technical field of development of computer software. The method comprises the following steps of: discretizing the workload of history projects and dividing the history projects into project classes in designated number; calculating the condition probability and the priori probability of each project attribution in the classes of the project workload by using workload attribute data of the history projects; establishing a Bayes classification model and predicting the workload class of the newly-added project; adding the newly-added project subjected to workload classification prediction into history project data, repairing missing date, recalculating the condition probability of the project attribute on the project workload class and recalculating the priori probability of the project workload class, and repeatedly iterating until all probability distributions are converged; and finally predicting the workload of the newly-added software project by using the converged posterior probability distribution. Compared with the prior art, by using the method, the capability of a mode for predicting the workload of the software project is greatly improved.

Description

technical field [0001] The invention relates to a method applied to computer software workload forecasting, in particular to a software workload forecasting method in the case of missing historical item attribute data, and belongs to the technical field of computer software development. Background technique [0002] Software cost estimation is the prediction of the cost attributes of software projects. Since the vast majority of software development costs are labor costs, software cost estimation usually refers to the estimation of workload (human cost). The importance of software workload estimation to software projects is reflected in: it is the basis for analyzing the feasibility of software projects, formulating software project budgets, and negotiating with software project stakeholders, it is an important basis for weighing software development strategies, and it is also an important basis for improving software processes and increasing productivity Important reference...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/44
Inventor 张文杨叶王青
Owner INST OF SOFTWARE - CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com