Text description generating method, system and device of crowdsourcing test report

Abstract

The invention discloses a text description generating method, system and device of a crowdsourcing test report. The method comprises the steps that screenshots and text descriptions in the test reportare acquired, key word sequences used for embodying test information in the text descriptions are determined, and hierarchical clustering is conducted on the screenshots according to a preset hierarchical clustering standard to obtain a plurality of sets of screenshot class clusters; the key word sequences which correspond to the test report and to which each screenshot in each set of screen capture class cluster belongs are input to a probability language model, and a class cluster text description generated by the probability language model is obtained; according to a preset classificationstrategy, the screenshot class cluster to which each to-be-diagnosed screenshot belongs in the to-be-diagnosed test report is determined, and a class cluster text description corresponding to the screenshot class cluster to which the to-be-diagnosed screenshot belongs is obtained. The number of the screenshots is large, the obtained text descriptions are rich, a developer can understand the test report, and the efficiency of diagnosing and understanding the test report is improved for the developer; the screenshots are objective, so that the diagnosis and understanding of the developer are noteasy to mislead.

Description

Technical field [0001] The present invention relates to the field of testing technology, in particular to a method, system and device for generating a text description of a crowdsourced test report. Background technique [0002] At present, because crowdsourcing testing can detect many errors in software testing, simulate real usage scenarios, and provide real user feedback, crowdsourcing testing is more suitable for mobile testing on mobile platforms. Crowdsourced testing will generate a large number of test reports during the mobile testing process. Test reports usually contain more screen shots and short text descriptions. In the prior art, the developer needs to diagnose and understand the test report to obtain the test result of the mobile platform. However, the number of test reports is relatively large, and the short text descriptions make the text information lack, resulting in low efficiency for developers to diagnose and understand test reports. In addition, crowdsour...

AI Technical Summary

Problems solved by technology

However, the number of test reports is relatively large, and the short text description makes the text information lacking, resulting in low efficiency for developers to diagnose and understand test reports

In addition, crowdsourcing testing will involve many end users. If the software engineering knowledge of end users is quite different, the text description in the test report will be subjective and not accurate enough, and may even mislead the developer's diagnosis and understanding.

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