512 lines
19 KiB
Markdown
512 lines
19 KiB
Markdown
-
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file:: [TOSEM-2023-0288_Proof_hi_1693841825871_0.pdf](../assets/TOSEM-2023-0288_Proof_hi_1693841825871_0.pdf)
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file-path:: ../assets/TOSEM-2023-0288_Proof_hi_1693841825871_0.pdf
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- comment updating dataset
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ls-type:: annotation
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hl-page:: 2
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hl-color:: yellow
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id:: 65198f61-6aab-4f71-84b8-04a3c5a51746
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- High-quality data is a key factor in deep learning-based approaches
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 6519908b-670b-4c0f-bfa7-6cf084367f32
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- Comment updating
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ls-type:: annotation
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hl-page:: 3
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hl-color:: blue
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id:: 65199206-3022-4f58-9825-618498bc74da
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- This task aims to automatically update the corresponding comments based on code changes made by developer
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 65199218-a454-4b39-997a-031ea97f4c0b
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- comment updating can take into account more information, such as code changes and old comment
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 65199327-ba6b-4525-939a-dcc07b4652b5
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- if comments are not updated in a timely manner, they may mislead future development activities.
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 65199337-7123-4dc5-a6c8-23f5536673e2
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- if obsolete TODO comments are not removed, they may introduce new bugs in future development [ 6]. For the task of comment updating, the data we expect is to be able to meet the real editing intention of developers and reflect the changes in code functionality
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 6519934c-807c-464b-a598-c43531e6dcea
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- Besides, comment updating datasets are often crawled from online repositories, such as Github, by extracting commit history versions
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 651993fb-82cc-4432-a5eb-2bdc37dee2c0
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- a noisy data sample in the comment updating task.
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 65199415-7d85-4913-bddf-e36fe63a8b31
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- From a code change perspective, the comment modification does not reflect the changes in the code.
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 6522dbea-ac76-4447-bef2-71012907b112
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- If the dataset contains a large number of such noisy data, the results generated by a model trained with the dataset would also be misleading
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ls-type:: annotation
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hl-page:: 3
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hl-color:: blue
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id:: 6522dc22-f58c-401d-b6a7-3076fff17688
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- Figure 2 shows an overview of our research procedur
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ls-type:: annotation
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hl-page:: 4
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hl-color:: green
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id:: 6522e59e-31d8-4013-9b60-e47ff0ac8d58
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- weak correlation between old and new comments,
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ls-type:: annotation
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hl-page:: 4
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hl-color:: blue
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id:: 6522eb18-02cb-4003-a0d7-8581843d20f3
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- weak correlation between code changes and comment changes
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ls-type:: annotation
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hl-page:: 4
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hl-color:: blue
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id:: 6522eb21-d417-453a-9308-c169d0e02032
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- CupCleaner
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6522eb2e-2901-4762-997a-a85be116406f
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- The first step is to design a criterion to calculate the score of all data and map them to a distribution
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ls-type:: annotation
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hl-page:: 4
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hl-color:: green
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id:: 6522eb52-eb79-4a98-b1ce-1320f4a48540
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- we design an anchor point search technique to adapt to different datasets. We are able to adaptively trim the tail of the score distribution, i.e., filter out the noisy data.
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ls-type:: annotation
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hl-page:: 4
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hl-color:: blue
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id:: 6522eba7-0d34-41cf-a392-8053d2061aac
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- mixing the noise data and high-quality data identified by CupCleaner
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ls-type:: annotation
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hl-page:: 4
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hl-color:: green
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id:: 6522ec49-663d-43d6-ab74-c0528507ae0c
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- The experimental results show that cleaning the training and validation sets with our approach can effectively improve the performance of the models on the same test set
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 65230fcc-e64a-4439-90ba-6980093950db
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- the prefromance of the model trained on the cleaned dataset still improves on all performance metrics.
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ls-type:: annotation
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hl-page:: 4
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hl-color:: green
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id:: 65230fe4-59f5-4925-8f7e-5c2b5283cd29
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hl-stamp:: 1697104112774
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- . We find that the evaluation performance on the cleaned test set is significantly better than that on the pure noise test set, regardless of whether the training set is cleaned or not.
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ls-type:: annotation
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hl-page:: 4
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hl-color:: yellow
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id:: 65231058-389d-460a-8848-2d7ae2c7bc02
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- 30% of the data in the training set
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6527c0ec-e2b1-4e07-a604-08cd13e590d4
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- we analyze the types of noises in this sectio
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ls-type:: annotation
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hl-page:: 5
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hl-color:: blue
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id:: 6527c209-af02-417d-ace0-63b94c12fb12
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- we define “noise” in the dataset as data samples where the target output cannot be generated based on the existing input
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6527c239-2278-4e06-bbbf-77bc08c30ddf
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- there is a significant semantic gap between the input and the output after an update
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6527c25b-b318-43a5-94eb-a2de19e37990
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- Below we discuss specific situations of these two types of weak correlations
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ls-type:: annotation
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hl-page:: 5
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hl-color:: yellow
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id:: 6527c3eb-297a-44c0-99c2-34cdb14e8a9d
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- invalid comments can be found among them
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ls-type:: annotation
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hl-page:: 5
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hl-color:: green
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id:: 6527c42a-9e06-422f-89f9-42008f798773
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- comments that consist of purely invalid characters and comments that only contain a single unrelated word do not fit the scenario of real updates
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ls-type:: annotation
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hl-page:: 5
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hl-color:: green
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id:: 6527c63f-b3fb-4384-9a8f-db163ca17ea3
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- new code has been deprecated
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ls-type:: annotation
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hl-page:: 6
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hl-color:: blue
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id:: 6527c660-e045-4759-9a79-a2d6c9bee964
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- correlation between the code changes and the corresponding comment updating may also be weak.
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ls-type:: annotation
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hl-page:: 6
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hl-color:: green
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id:: 6527c67f-a00e-4fda-a819-e76337de3bec
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- if the code is significantly modified but the comments barely change, it belongs to this type of noise
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ls-type:: annotation
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hl-page:: 6
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hl-color:: blue
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id:: 6527c68a-1ca0-4ee1-9a25-a8ca3da895c9
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- Similarly, if the code barely changes while the comments need to be significantly modified, this can also mislead the model.
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ls-type:: annotation
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hl-page:: 6
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hl-color:: blue
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id:: 6527c6c6-b750-4172-85cd-908563a8440c
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- oise can exist in data where both comments and code undergo changes but their changes are not related to each other.
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ls-type:: annotation
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hl-page:: 6
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hl-color:: blue
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id:: 6527c6e3-a425-4038-b5d0-85f2b49eac3d
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- In summary, weak correlations between comments and code, as well as weak correlations between comment changes and code changes, are typical reasons for data noise in datasets for comment updating. These noisy data can mislead the model’s generation process, thus reducing the overall performance. Therefore, we may need a criteria to evaluate the quality of individual data samples and filter out noises based on their data quality scores
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ls-type:: annotation
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hl-page:: 6
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hl-color:: purple
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id:: 6527c751-fdb2-4348-8cb2-77a8bb9ce1b2
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- anchor to filter out the noising data
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ls-type:: annotation
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hl-page:: 6
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hl-color:: yellow
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id:: 6527c78e-33c1-4cce-ae58-79cf5b88a983
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- we designed a criterion to calculate the quality scores for each data samp
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ls-type:: annotation
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hl-page:: 6
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hl-color:: blue
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id:: 6527ea08-470e-48cd-a341-246bfa44dbf5
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- internal semantic similarity
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ls-type:: annotation
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hl-page:: 6
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hl-color:: purple
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id:: 6527ea11-4785-4a7b-acc7-ed57e72e64bb
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- Weak correlations existing between comments and code (Type I)
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ls-type:: annotation
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hl-page:: 6
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hl-color:: green
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id:: 6527ea21-7aca-4a90-9625-8689f2281167
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hl-stamp:: 1697115132593
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- r internal semantic similarity
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ls-type:: annotation
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hl-page:: 6
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hl-color:: yellow
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id:: 6527ea2b-87d4-411c-9f87-6ea17e5dff20
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- . It is worth noting that the semantic representation mentioned above is at the token level.
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ls-type:: annotation
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hl-page:: 6
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hl-color:: blue
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id:: 6527eb2c-3d1e-496a-95fc-f0bd677b27e4
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- We choose the state-of-the-art model pre-trained on code-related datasets, GraphCodeBert [ 8 ], to calculate the semantics of comments or cod
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ls-type:: annotation
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hl-page:: 6
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hl-color:: green
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id:: 6527eb30-56b8-4463-8dca-b866af730ee3
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- cosine similarity of semantic embeddings to describe the correlation between old and new comments, and between old and new code, respectively
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ls-type:: annotation
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hl-page:: 6
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hl-color:: purple
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id:: 6527eb4f-5388-42aa-9b20-6252653d43f0
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- Sentence-BERT helps in capturing natural language semantic information to enhance the overall performance and robustness of the evaluation. Finally, we compute the evaluation score 𝑆1 for identifying noise in type I
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 6527ebde-d308-4bfa-b756-f1357554b3e2
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- Weak correlation existing between comment changes and code changes (Type II)
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ls-type:: annotation
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hl-page:: 7
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hl-color:: green
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id:: 6527ec07-8c4d-491c-bd5d-abed4cf3c0cd
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- nconsistencies between comment changes and code changes
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ls-type:: annotation
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hl-page:: 7
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hl-color:: blue
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id:: 6527ec5d-8a25-4040-9011-35f017bda998
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- comment updating task
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ls-type:: annotation
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hl-page:: 9
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hl-color:: blue
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id:: 6527ed40-630d-44d2-86be-06711677011d
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- pdated comment
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ls-type:: annotation
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hl-page:: 9
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hl-color:: blue
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id:: 6527ed50-1f6f-4ad9-be71-de06a3c61a27
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hl-stamp:: 1697122949958
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- metrics for evaluating the effectiveness
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ls-type:: annotation
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hl-page:: 9
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hl-color:: blue
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id:: 65280a83-cf57-4801-b278-342075aba5b8
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- baselines
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ls-type:: annotation
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hl-page:: 9
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hl-color:: blue
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id:: 65280a8a-682c-4fe4-a495-96eb196316c4
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- experiment settings
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ls-type:: annotation
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hl-page:: 9
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hl-color:: blue
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id:: 65280a8f-37f8-4c01-b348-8596dc431019
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- Whether
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ls-type:: annotation
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hl-page:: 9
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hl-color:: red
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id:: 65280a9e-80e8-4c23-82b7-0b3ab47c7501
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- changes in the number of data
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ls-type:: annotation
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hl-page:: 9
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hl-color:: green
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id:: 65280b47-e7d9-4af7-84b0-3050101288de
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- xamples that CupCleaner identifies as noisy and high-quality data,
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ls-type:: annotation
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hl-page:: 9
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hl-color:: blue
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id:: 65280bb0-8987-4b66-9bd8-f3d4821482a2
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- human evaluation on the noisy and high-quality data identified by CupCleane
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ls-type:: annotation
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hl-page:: 9
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hl-color:: blue
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id:: 65280bb8-4b66-40e2-a189-ca3d373cdd49
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- Q2: How effective is the training data cleaned by CupCleaner?
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ls-type:: annotation
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hl-page:: 9
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hl-color:: green
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id:: 65280bc1-dd33-4b4c-b521-b847608eeb33
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- CupCleaner to clean only the training and validation sets
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65280bf7-8a02-4e7f-bb1e-dce324699404
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- comparing the performance of the models trained on the cleaned dataset with the models trained on the original dataset
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ls-type:: annotation
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hl-page:: 10
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hl-color:: blue
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id:: 65280c01-34a8-48c7-9f34-86e59c418846
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- RQ3: How effective is the use of CupCleaner-cleaned data for evaluating models?
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ls-type:: annotation
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hl-page:: 10
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hl-color:: yellow
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id:: 65280c1c-24d1-4a81-bc17-dbec3d6ab178
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hl-stamp:: 1697126043388
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- different versions of the test set using the best-performing model from the previous research question.
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65281639-713e-4ff7-921e-6fb204174b3b
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- RQ4: How efficient is CupCleaner?
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 6528166d-bbb6-4570-9178-041617f45ebb
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hl-stamp:: 1697126045961
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- we calculate the time consumption for cleaning the entire dataset, including the time required to compute scores and filter out the data
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65281694-0f23-4f97-bb44-691e9c91a6e7
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- able 1 describes the basic statistics of these three datasets.
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65281b6d-8201-421e-be12-794ebdc336a8
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- Panthaplackel et al., 2020 dataset collects 7.2k data samples from the commit history of open-source Java projects on Github
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65281bb4-a458-45d0-b6a1-afd7d6980e06
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- Liu et al., 2020 dataset is constructed from 1,063K method-doc co-change instances from Github, resulting in 104k data samples
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65281bbd-0377-41e6-85cc-b25f16325046
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- Panthaplackel et al., 2021 dataset is originally designed for comment consistency detection, but some of the data can also be used for the comment updating task
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65281bc7-fd1d-43f4-ac76-4d9be7d8299f
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- We extract the data with comment changes from this dataset to construct a new comment updating dataset, and discard the data with no changes in the comments.
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ls-type:: annotation
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hl-page:: 10
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hl-color:: yellow
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id:: 65281c21-7981-447a-9611-a880f5781076
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- However, due to a lack of exploration into the characteristics of comment updating, the constructed datasets still contain a significant amount of noisy data
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ls-type:: annotation
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hl-page:: 11
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hl-color:: yellow
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id:: 65281c6a-e936-4cd6-aac0-d7fc79cf8632
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- oken level
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 65281cb8-75c4-4555-b8d1-aae95c63a161
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- e sentence level
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 65281cbc-9398-4989-bbf8-bf86684392ae
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- evaluation metrics in the field of text generation
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 65281cc5-b0a4-4d47-b42f-bd0d44565f40
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- align
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ls-type:: annotation
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hl-page:: 11
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hl-color:: yellow
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id:: 65281d03-bc13-4652-aaab-197042fe3e1e
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- valuation metric based on edit distance, originally designed to assess the overall quality of text simplification.
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 65281d43-dd7a-4892-b487-4dcc5d352db9
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- GLEU is closer to human-level judgment than BLEU and is more suitable for tasks involving edits
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 65281d78-6b37-462f-b679-f28724513791
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- PLBART
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ls-type:: annotation
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hl-page:: 12
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hl-color:: purple
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id:: 65281df5-34e7-4335-80e7-73c907dc3802
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- he experimental results show that UniXcoder achieves improvements on multiple code-related tasks.
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ls-type:: annotation
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hl-page:: 12
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hl-color:: purple
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id:: 65281e15-0e25-43ed-bded-06f26fb91853
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- The experimental results demonstrate that PLBART outperforms CodeGPT[ 18 ] and CodeBERT[ 5 ] in multiple code understanding and code generation tasks
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ls-type:: annotation
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hl-page:: 12
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hl-color:: purple
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id:: 65281e1e-0235-4784-810a-60d8435d3d0a
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- 188GB RAM
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ls-type:: annotation
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hl-page:: 12
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hl-color:: purple
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id:: 65281e56-7510-44fc-9b22-53b9e89d5c9d
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- NVIDIA TITAN RTX GPU with 24GB of memory.
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ls-type:: annotation
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hl-page:: 12
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hl-color:: purple
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id:: 65281e5d-e30b-4338-abfe-6c0ed7002216
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- 20
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ls-type:: annotation
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hl-page:: 12
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hl-color:: purple
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id:: 65281e63-04fc-48df-934a-b1b687e0951b
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- terminate the training process if the experimental performance on the validation set does not improve within three consecutive epochs. F
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ls-type:: annotation
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hl-page:: 12
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hl-color:: purple
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id:: 65281e6f-e9ea-4ab6-9352-51c962ce0602
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- The red dashed line in the second step of Figure 2 (Sec.3) corresponds approximately to0.65, and the green solid line corresponds to 0.8 in the score distributio
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ls-type:: annotation
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hl-page:: 13
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hl-color:: yellow
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id:: 65282097-c213-4409-93a4-fcec00a651ad
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- The final scores
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ls-type:: annotation
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hl-page:: 13
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hl-color:: yellow
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||
id:: 65282108-3ce6-4d81-9758-39974f55389a
|
||
- As shown in Table 2, we filter out approximately 33%, 12%, and 18% of the data in the three datasets, respectively.
|
||
ls-type:: annotation
|
||
hl-page:: 13
|
||
hl-color:: yellow
|
||
id:: 65282238-75a8-43c9-87b5-01a0f937dfcb
|
||
- “Would you accept this data sample into the comment updating dataset?"
|
||
ls-type:: annotation
|
||
hl-page:: 14
|
||
hl-color:: green
|
||
id:: 652822d1-4eea-4c87-bc4b-dc3d3c9c1fd4
|
||
- The label for each data sample ranges from 1 to 5
|
||
ls-type:: annotation
|
||
hl-page:: 14
|
||
hl-color:: green
|
||
id:: 652822fe-ebd6-4ef4-8eea-41a38feec147
|
||
- This indicates that the majority of the data filtered out by CupCleaner belong to noisy data, while the data retained is predominantly in line with the intent of the comment updating task.
|
||
ls-type:: annotation
|
||
hl-page:: 14
|
||
hl-color:: blue
|
||
id:: 6528232d-b8f4-4993-983a-d3aa4f8e0374
|
||
- This indicates that CupCleaner can distinguish noisy data from high-quality data in comment updating datasets
|
||
ls-type:: annotation
|
||
hl-page:: 15
|
||
hl-color:: green
|
||
id:: 6528234b-e57e-40ec-a50c-ce2a23319b41
|
||
- original, random, and cleaned
|
||
ls-type:: annotation
|
||
hl-page:: 16
|
||
hl-color:: green
|
||
id:: 65282382-46ef-49d3-b737-ec22905d30e2
|
||
- subset of the original data with the same size as the cleaned data
|
||
ls-type:: annotation
|
||
hl-page:: 16
|
||
hl-color:: green
|
||
id:: 652823d4-f34c-4535-a478-5f27586bbc15
|
||
- the original test set remained unchanged
|
||
ls-type:: annotation
|
||
hl-page:: 16
|
||
hl-color:: green
|
||
id:: 652823ea-211b-4cb8-9460-d90a7e31443a
|
||
hl-stamp:: 1697129696172
|
||
- we process only the training and validation sets
|
||
ls-type:: annotation
|
||
hl-page:: 16
|
||
hl-color:: yellow
|
||
id:: 652823f0-2a17-43c6-b49b-52874a56768e
|
||
hl-stamp:: 1697129491533
|
||
- CupCleaner shows its effectiveness regardless of whether the model performs well on the original dataset or whether the original data is heavily contaminated with noises.
|
||
ls-type:: annotation
|
||
hl-page:: 16
|
||
hl-color:: green
|
||
id:: 652824ee-8b64-401d-870b-a98d079b402a
|
||
- The data with scores higher than the anchor point are assigned to the cleaned version of the test set, while the rest are assigned to the noisy version of the test set.
|
||
ls-type:: annotation
|
||
hl-page:: 16
|
||
hl-color:: green
|
||
id:: 65282518-469b-4d09-8c6b-4d691d13b2b9
|
||
- This indicates that the cleaned test set may better reflect the model’s real performance, and also shows that CupCleaner may help in constructing datasets for accurate measurement of model abilities
|
||
ls-type:: annotation
|
||
hl-page:: 17
|
||
hl-color:: green
|
||
id:: 6528254b-bb05-469d-88b5-fedfd7543dba
|
||
- his indicates that our data cleaning approach can produce cleaned data within an acceptable time
|
||
ls-type:: annotation
|
||
hl-page:: 18
|
||
hl-color:: green
|
||
id:: 65282593-ddec-49be-a400-bc3a4fbbae40
|
||
- esults of human evaluation also demonstrate that our cleaned data aligns better with the scenarios and intentions of the comment updating tasks
|
||
ls-type:: annotation
|
||
hl-page:: 18
|
||
hl-color:: green
|
||
id:: 652825e3-00c1-4b1c-81ad-ffc1424c4000
|
||
- CupCleaner, a data cleaning approach for comment updating datasets. The data cleaning strategy of CupCleaner mainly consists of two steps. First, we design a criterion to calculate the quality scores for all data samples, and then we remove the tail of the score distribution.
|
||
ls-type:: annotation
|
||
hl-page:: 20
|
||
hl-color:: purple
|
||
id:: 65282606-3f49-4d98-bd46-501d37963ac4
|
||
- scoring process, we consider the correlations within the comments or code, as well as the correlation between code changes and comment changes
|
||
ls-type:: annotation
|
||
hl-page:: 20
|
||
hl-color:: yellow
|
||
id:: 65282611-a2df-42a5-bbea-d654edd9fda3
|
||
- CupCleaner can effectively filter out noisy data and improve the performance of the models without changing the test set
|
||
ls-type:: annotation
|
||
hl-page:: 20
|
||
hl-color:: purple
|
||
id:: 6528266e-744e-4067-a4a9-06da1fec5d40
|
||
- expand our data cleaning approach
|
||
ls-type:: annotation
|
||
hl-page:: 20
|
||
hl-color:: yellow
|
||
id:: 652826af-5114-42ef-9ad1-b858e9db5237
|
||
hl-stamp:: 1697130161212 |