A Method of Building Software Defect Prediction Model Based on Compiler Intermediate Representation

Abstract

The invention discloses a method for constructing a software defect prediction model based on a compiler intermediate representation, comprising the following steps: step (A), using a compiler to convert a source code or a binary program into a compiler intermediate representation, that is, an IR instruction sequence ; Step (B), constructing a program control flow graph CFG (CV, CE) based on the IR instruction sequence through semantic analysis of the IR instruction sequence. The present invention builds a software defect prediction model based on the intermediate representation of the compiler. On the one hand, it can get rid of the excessive dependence of sample data on source programs. On the other hand, it can combine different types of source programs for analysis, greatly expanding the breadth and depth of sample data. , which is extremely important for the construction of a big data-driven software defect prediction model. Building a software defect prediction model based on compiler intermediate representation will hopefully replace the source code-based software defect prediction model and become an important research field of software defect prediction. breaking point.

Description

A method for building software defect prediction model based on compiler intermediate representation technical field The present invention relates to software engineering and software defect prediction technical field, be specifically related to a kind of intermediate table based on compiler The software defect prediction model construction method shown. Background technique As people's demand for software is increasing day by day, its functions are more and more, and the complexity is also higher and higher. While benefiting people's lives, it also brings many hidden dangers. Software defects have become many system errors, failures, crashes and even Potential root cause of machine crashes. Software defect prediction technology associates software defects with various software by building a software defect model. The metric vector is correlated to realize the preliminary positioning of the modules that may have defects in the software, so as to optimize the softwar...

AI Technical Summary

Problems solved by technology

If the collected samples are classified and analyzed according to the programming language and system architecture, the correlation between defect samples will be segmented, so that the utilization rate of the originally limited defect samples will be greatly reduced

At the same time, limited by commercial privacy, it is still difficult for researchers to obtain source codes in practical applications, and software defects in these commercial applications are extremely important sample data

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