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Crowdsourcing test report clustering method based on AP affinity propagation algorithm

A test report, neighbor propagation technology, applied in software testing/debugging, computing, computer parts and other directions, can solve problems such as difficulty in accurately calculating the similarity of test reports, and difficulty in achieving expected results in functions and performance, to overcome similarities. The effect of inaccurate degree calculation, solving semantic deviation, and improving accuracy

Pending Publication Date: 2021-12-10
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

Therefore, it is difficult to accurately calculate the similarity between test reports, which makes the current crowdsourcing test report clustering methods difficult to achieve the expected results in terms of function and performance.

Method used

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  • Crowdsourcing test report clustering method based on AP affinity propagation algorithm
  • Crowdsourcing test report clustering method based on AP affinity propagation algorithm
  • Crowdsourcing test report clustering method based on AP affinity propagation algorithm

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Embodiment Construction

[0029] Data source acquisition: The data sets used in this experiment include five mobile application crowdsourcing test report data sets of JustForFun, SE-1800, iShopping, CloudMusic and UBook. These datasets are collected by inviting a large number of crowdworkers to perform crowdsourced tests on 5 mobile apps on the Kikbug platform. These five datasets were assigned to 3 graduate students and asked to independently complete the data labeling task, which is to classify the test reports according to the defects revealed. When a new defect is detected, the annotator needs to use some summary words to describe the defect and create a new cluster. When reviewing another test report and detecting the same defect, the annotator should put this test report in this cluster. in the cluster. The annotation results of each annotator need to be manually checked, and at least two annotators are required to assign them to the same cluster when they think that the two test reports reveal ...

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Abstract

The invention discloses a crowdsourcing test report clustering method based on an AP affinity propagation algorithm. According to the method, firstly, keywords are extracted by utilizing a natural language processing technology, and identical or similar words are replaced by the same word by utilizing a synonym forest, so that the semantic deviation problem caused by different words is effectively solved. Then an asymmetric similarity strategy is adopted to calculate the similarity between test reports, and the strategy can effectively solve the problem that the similarity calculation is inaccurate under the condition that the length difference of the test reports is large. Finally, the test reports are clustered by using an AP affinity propagation clustering algorithm. Through the method, a crowdsourcing test report clustering effect can be improved, a developer only needs to select one test report from each cluster for examination, the examination time of the developer is greatly shortened, and the method has very important significance on software maintenance.

Description

technical field [0001] The invention relates to the field of software maintenance, in particular to a crowdsourcing test report clustering method based on an AP neighbor propagation algorithm. Background technique [0002] In crowdsourced testing, crowd workers help developers execute tests and submit test reports. Developers need to manually review the submitted test reports, and determine software problems according to the description of the test reports. source file and fix it. In software maintenance, crowdsourcing test reports are a very important resource, which can help developers improve software systems and improve software quality. Compared with traditional software testing, crowdsourcing testing has obvious advantages. First of all, crowdsourcing testing recruits not only professional testers, but also end users for testing, so that more realistic user experience information can be obtained. Secondly, crowdsourcing testing recruits a huge number of crowdsourcin...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F11/36G06F40/30G06F40/289G06F40/216G06F40/247
CPCG06F11/3692G06F40/30G06F40/289G06F40/216G06F40/247G06F18/23G06F18/24147
Inventor 陈信孙添俞东进程世超
Owner HANGZHOU DIANZI UNIV
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