Abstract
With the increasing for function, scale, hierarchy and complexity of software project, the software life cycle and development stage show a trend of cross-cutting and fuzzy boundary. The non-technical factors, such as poor management and control during the implementation of software projects, are the major reason for causing the low success rate of software projects recently. Therefore, the software quality evaluation under complex environment should take the cross-influence between different stages of software life cycle and different quality evaluation standards into consideration. Our research is to construct a new software quality evaluation model by using the influence relationship and the influence intensity index between project management domain and project quality evaluation criteria including scope, cost, and time. First, we came up with the definition of software project management domain in the process of software project development and management. Second, we proposed a mathematical method for extracting the direct or indirect influence relation between them, and give a definition for the quantitative evaluation index and its calculation formula. At last we proposed to construct a neural network training model which includes evaluation model logic relationships and software quality quantitative evaluation index. Through study and training by simulated software project management data, we can discover some key data, such as normal threshold range of influence, factor weights, etc. Therefore, a complete evaluation system is built, and the scientific nature and accuracy of the proposal evaluation system will be improved.
Highlights
According to the Standish group’s 2015 report, about 70% of large software projects in the world are unsuccessful, either planned to be delayed, or over budgeted, or lacking in functionality etc. [1]
In order to improve the scientific and accuracy of evaluation system, we will build neural network training model including logical relationship model as well as SCT quantitative evaluation index based on artificial neural network technology
SUMMARY OF CONCLUSION In this article, we propose a software project quality evaluation system based on software project management data and quality evaluation standard SCT
Summary
According to the Standish group’s 2015 report, about 70% of large software projects in the world are unsuccessful, either planned to be delayed, or over budgeted, or lacking in functionality etc. [1]. Based on the proposed method above, it solves the extraction and analysis logical relationship problems among the management domain, sub-management domain and the SCT evaluation standard, so as to provide theoretical basis and solutions for software project management data logical relational model data mining. We establish the BP neural network training model based on SCT evaluation criteria and software management domain (sub-management domain).’’ From this perspective, the direct or indirect contribution rate of any sub-management domain and the direct or indirect cumulative contribution rate of the management domain can be calculated through the above formula, providing data basis for quantitative analysis and evaluation of the whole project. D. THE ESTABLISHMENT OF TRAINING MODEL BASED ON BP NEURAL NETWORK The above research solves the quantitative evaluation issue of software project quality, and improves the scientific and objectivity of our proposal. It can be seen from the experimental result diagram that the measured value and the training value are close to coincide, which indicates that the BP neural network model designed in this article can better reflect the characteristics of the simulation data, and can be well applied in our proposal in this article
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
