Method for improving accuracy of quality forecast of class hierarchy in object-oriented software

Abstract

The invention relates to a method for improving the accuracy of quality forecast of a class hierarchy in object-oriented software in the technical field of software development. The method comprises the following steps: using a software metrics set to convert classes in the object-oriented software into corresponding characteristic vectors; packaging all the classes in the class hierarchy into a knowledge representation form of a tree structure through a tree-form data structure, wherein the tree structure comprises the characteristic vectors of all the classes of the class hierarchy and structure information and inheritance relationships between the classes; and using an improved support vector machine forecast mechanism of a layering core. For an object-oriented software system, the invention discloses a method for training a set of integral software quality forecasting models, and the forecasting models trained by the method have extensive application range, are associated with practices closely, have high accuracy and high reliability, and are essential for ensuring high-efficiency and high-accuracy forecasts of the quality of software modules.

Description

technical field [0001] The invention relates to a method in the technical field of software development, in particular to a method for improving the quality prediction accuracy of Class Hierarchy in object-oriented software. Background technique [0002] The software quality model used in the software quality prediction system needs to reasonably reflect the attribute characteristics and functional utility of each software module. The degree to which it can effectively describe the probability distribution of the software module feature space determines the performance of software quality prediction (accuracy, reliability, etc.) . For large-scale industrial software, finding and locating possible errors in the software as early as possible can save labor costs, shorten software development time, and improve product quality and customer satisfaction. The usual software quality prediction is based on software metrics. By training and learning data sets from earlier or similar...

AI Technical Summary

Problems solved by technology

[0005] The above-mentioned software quality prediction methods are to carry out fixed modularization of software according to a certain size and level, and after software measurement and feature extraction, some mathematical statistics or learning methods are adopted for training and prediction. Such a training method can achieve It has a certain prediction purpose and has many practical applications, but there are still certain limitations: firstly, in the process of software modularization, if the division is too small, the differences between different modules will be small, making it difficult to distinguish and locate ; If the division is too large, the software quality prediction positioning will be too broad, and the purpose of prediction will not be achieved

At present, there are relatively few modularization methods for object-oriented software. There are only a few types of basis such as functions, methods, and classes.

Secondly, due to the inheritance characteristics of object-oriented software, a large part of the structured information is lost during the process of modularizing the software into various or smaller modules, which makes it difficult to improve the accuracy of the final software quality prediction.

Images