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Rewrite score connecter #200
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From what I can see, there are many areas for improvement in this file. Although our changes are small, we can try to do better with each modification.
Moreover, many steps and variables in this PR are named after HTML elements, making it difficult to understand why these steps appear in the data conversion process. For example, what is h3Node
? What table does tableNodes
refer to? And so on.
If possible, I'd suggest adding some comments, giving variables more descriptive names, or documenting in external files to clarify what these steps are actually doing. Em... I know it's hard...
Regarding the description of the PR, I have a few suggestions: Firstly, in the 'Description' section, you only wrote 'Problem', and the 'Solution' part may be not detailed enough. You mentioned that you changed the algorithm, but what we should be referring to is 'the process of data transformation'. We may add more details about what exactly has been changed, which key steps are involved, and what is the root of the problem, etc. Additionally, for the 'How to Verify' section, I recommend writing from a QA perspective. For instance, the first point, "Test the score connector with the data provided by the user," would be hard to understand for the verifier if they are not familiar with what a ' Lastly, I'm not quite sure what Well... above are just some personal viewpoints by me, You may feel free to change anything :) |
So I dropped the invalid row by checking the number of columns in that row since it will push the valid pending data in the list, making the converting process in the for loop produce unexpected results.
As I just did, I dropped the invalid semester by checking if there was a score table right next to the semester h3 element.
...
result = await Connector.getDataByGet(parameter);
result='''<html>... </html>''';//<-HTML text String here
tagNode = parse(result);
... |
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LGTM.
Remind that we should ensure this pr can really solve the problem before merging.
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LGTM.
Description
A user has a dropout record in the QryRank/QryScore inquiry result, which is not expected in the present data extraction algorithm, so I changed the algorithm and tried to fix this error.
How to Verify?
Screenshots/GIF/Test Results (Optional)