Collapse stack data-based software defect code locating method

Abstract

The invention relates to a collapse stack data-based software defect code locating method. Historical collapse stack data are combined with historical code maintenance records; by a machine learning technique, a defect code prediction model is established; and various final target collapse code units including defects in software are predicted, so that the code units including the defects in the software are rapidly located; evaluations are carried out on the final target collapse code units; and the work efficiency of restoring software codes is effectively improved. The collapse stack data-based software defect code locating method is simple in design method and suitable for different types of programming languages and different scales of computer software, and has scalability and adaptability; a parallel algorithm is convenient to use; a lot of collapse stack data can be rapidly and effectively analyzed and processed; and the software defect locating efficiency is improved; and the collapse stack data-based software defect code locating method can be applied to testing and maintaining work of medium-to-large software systems.

Description

technical field [0001] The invention relates to a method for locating software defect codes based on crash stack data, and belongs to the technical field of software testing and maintenance. Background technique [0002] Software defects are inevitably generated during the development and maintenance of computer software. Defect location is a time-consuming and labor-intensive task in the software debugging process, especially for large-scale software systems. Software defect location has always been one of the hot issues in the field of software engineering research. Defect location uses code execution information and defect (problem) reports to predict the possible scope of defective code and assist developers in finding defect locations. The scale of the current software system is getting bigger and bigger, and there are more and more code files. According to the content of the defect report, the process of locating the defect code from the massive code files can be desc...

AI Technical Summary

Problems solved by technology

Also, in the software development cycle, there is also a contrast between the large size of the software and the limited development and testing costs

[0003] At present, defect code positioning is still mainly based on manual positioning and empirical judgment. Interactive debugging tools are often used, which requires a lot of manual intervention, low positioning efficiency, and consumes a lot of time and cost. It is not repeatable, and it is difficult to meet and adapt to large-scale software systems. Debugging requirements

In recent years, researchers have proposed some automatic defect code location methods, but most of these methods need to obtain dynamic information of the software execution process, such as code coverage data and execution trajectory, etc., which are suitable for small-scale statement-level software defect location; Defect localization requirements for large-scale software systems at the function and process levels

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