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A Defect Location Method for Webpage Automation Testing Based on Deep Learning
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A technology of automated testing and deep learning, applied in the field of defect location, to achieve precise location, efficient completion of test tasks, and accurate error defects.
Active Publication Date: 2021-08-13
TIANJIN UNIV
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[0013] The present invention provides a method for locating defects in webpage automation testing based on deep learning. The present invention solves the problem of the accuracy of keyword extraction due to ambiguity in natural language in traditional demand-based automation methods, the problem of accurately locating target elements of webpages, and Execute automated testing operations to accurately locate the defects of the system under test. See the description below for details:
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Embodiment 1
[0033] The invention discloses a method for locating defects in web page automation testing based on deep learning, see figure 1 , the method includes the following steps:
[0034] 101: Use Chinese punctuation marks and Chinese conjunctions to analyze the test case, decompose it into the smallest operation unit, and store it in the database with the attribute structure of "test case-test step-segmentation method-operation unit" as the operation unit ;
[0035] 102: Extract test operation information from the smallest operation unit based on deep learning (that is, neural network), obtain a keyword sequence, and define the keyword sequence as a triplet sequence;
[0036] 103: Crawling the webpage, parsing the webpage into an HTML DOM tree and traversing the HTML DOM tree, and locating the target page element set through the Target element in the triple sequence;
[0037] 104: Call the operation interface through the triple sequence, pass in the operation information, and auto...
Embodiment 2
[0042] The following is combined with specific calculation formulas, examples, figure 2 with image 3 The scheme in Example 1 is further introduced, see the following description for details:
[0043] 201: Analyze test requirements and obtain operating units;
[0044] Definition 1 (set of test requirements):
[0045] T={t 1 , t 2 ,...,t m} indicates the test case set of the system under test written by the tester, which is used to describe the instruction operation in the functional test. These test cases are relatively independent and have no dependencies. The target element of the user interface of the test application, where t iIndicates the ith test case of the test case set.
[0046] Firstly, test cases are extracted from the test requirement set, and each test step is segmented according to Chinese punctuation and conjunctions, and the segments are sorted to form an operation unit sequence with an operation sequence.
[0047] Definition 2 (operating unit set): ...
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Abstract
The invention discloses a method for locating defects in web page automation testing based on deep learning, which includes: using Chinese punctuation marks and Chinese conjunctions to analyze the test case, decomposing it into the smallest operation unit, and using "test case - test step - cut "Sub-mode-operating unit" is the attribute structure of the operating unit and stored in the database; based on deep learning, the smallest operating unit is used to extract the test operation information, obtain the keyword sequence, and define the keyword sequence as a triple sequence; crawl Web page, parse the web page into an HTML DOM tree and traverse the HTML DOM tree, locate the target page element set through the Target element in the triple sequence; call the operation interface through the triple sequence, pass in the operation information, and automatically generate test cases The script, through the backtracking algorithm, explores multiple test streams based on the test results of the target page element set; drives the real browser to accurately locate the defects of the system under test.
Description
technical field [0001] The invention relates to the field of defect location, in particular to a method for locating defects in web page automation testing based on deep learning. Background technique [0002] Based on artificial intelligence (AI) technology such as: pattern-based reasoning and search strategies, mainly relying on the support of a set of rule systems [1] . The automation of software requirements is also based on natural language analysis, which is designed for situations expressed in natural language. For example: the need for natural language expression for detailed semantic analysis, but often the problem of unavailable semantic information [2] . Semantic-based keyword sequences are extracted from test cases described in English [3] , which is used for computers to understand operating instructions, and automatically synthesizes scripts that can be used for automated testing. ATA [3] It marks the part of speech of the test case through POS, designs t...
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