Chinese crowdsourcing test report clustering method based on semantic similarity

Abstract

The invention discloses a Chinese crowdsourcing test report clustering method based on semantic similarity, and the method comprises the steps: inputting a Chinese test report set, removing invalid test reports, and carrying out the sentence segmentation of effective test reports, and obtaining a test report sentence pair data set; constructing a test report sentence pair training set to train the semantic similarity model to obtain a semantic similarity calculation model; inputting the test report sentence pair data set into a semantic similarity calculation model for semantic similarity calculation to obtain a test report similarity matrix; setting an expected bug number of the test item, and performing spectral clustering according to the test report similarity matrix to obtain a test report class cluster; and decomposing the test report similarity matrix according to the test report class clusters to obtain the test report similarity matrix of each class cluster, and calculating a test report with an accumulated similarity score Top-5 in each class cluster as a final output result. The test report review automation process of the crowdsourcing test platform is improved, and the test report review efficiency is effectively improved.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a Chinese crowdsourcing test report clustering method based on semantic similarity. Background technique [0002] In the process of crowdsourcing software testing, crowdsourcing workers discover and submit problems that arise during the use of the software, write a test report and submit it to the tested party in exchange for remuneration. A crowdsourcing testing project usually receives hundreds or thousands of test reports. According to research, less than 50% of the submitted software problem reports reveal real defects in the software, with an average of 82%. Crowdtest reports are duplicated. It will consume a lot of time and manpower testing cost if the report set containing a lot of duplicates and false positives is checked manually by the tested party. Therefore, it is very necessary to remove duplicate reports and false positive reports efficiently and automatica...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a Chinese crowdsourcing test report clustering method based on semantic similarity, which solves the problem of false positives and repeated reports in the review process of existing Chinese crowdsourcing test reports, and the human resource consumption of manual review is large and the efficiency is low. low technical problems

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