Test program plagiarism detection method based on support vector machine

Abstract

The invention relates to a test program plagiarism detection method based on a support vector machine. The method comprises: performing cutting and static analysis on a to-be-tested program and a testprogram to obtain a to-be-tested method mapping set and a test method mapping set; secondly, traversing the players pairwise, calculating the similarity of the test fragments, and summarizing to obtain a similarity set; secondly, selecting an appropriate kernel function and a reference point to establish a support vector machine model, and optimizing the support vector machine model; and finally,for other test programs, calculating a similarity set and inputting the similarity set into the support vector machine to judge plagiarism conditions among the test programs. The invention aims to fill the blank of a test program code similarity detection technology. The code plagiarism detection accuracy and precision of the test program are improved, so that developers are helped to test code plagiarism behaviors of competitors for competition automation detection, the manual detection link is omitted, the labor cost and the time cost are saved, and it is guaranteed that the competition ismore fair and justice.

Description

technical field [0001] The invention belongs to the field of plagiarism detection of software test codes, and especially solves the phenomenon of plagiarism in test codes submitted by players in current developer test competitions more accurately. After analyzing the program to be tested and the test program, model training, testing, verification and optimization are carried out on the existing test code data set by introducing the support vector machine method. Then, classify the unlabeled test code data. By analyzing the category of the test codes, it is possible to determine whether there is plagiarism between the test codes, thereby eliminating the need for manual inspection, saving labor costs and time costs, and ensuring a fairer competition. Background technique [0002] In the process of writing code, it has become a common behavior for software developers to copy and paste code from different sources and modify it to achieve their own goals, which improves the effi...

AI Technical Summary

Problems solved by technology

[0003] For unit test test case code similarity detection, there are currently no relatively mature applications or tools in academia and industry

Moreover, because contestants often only copy and paste the code of a few test cases, different unit test cases are completely independent, unlike source codes that have dependencies; and contestants will also perform a certain amount of test case code Modifications (for example, modifying literals, inserting and deleting one or several statements), so that the code similarity detection of test cases directly performed by existing source code similarity detection tools often cannot truly reflect the plagiarism of test codes, affecting Analysis accuracy for tools like code cloning, plagiarism checker, etc.

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