cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11613247
title: Kevin: Knowledge-Enhanced Conventional Commit Message Generation

description: Introduction


We purpose a novel conventional commit message generation framework Kevin. In this directory, we give all code, dataset, and experiment results.


We use the dataset MCMD to evaluate our model, and filter some meaningless data on [`it`](dataset/MCMD_filter.zip).


Now we introduce the purpose of each folder:


1. `code`: this is the implement of our approach.

    1. `commit_type_classification`: this is implement of our approach in commit type classification, and there are detailed documents in this folder.

    2. `commit_description_classification`: this is implement of our approach in commit description generation, and there are detailed documents in this folder.

2. `dataset`: this is the dataset we used to evaluate our approach, `MCMD_filter.zip` contains the filtered version of the dataset MCDM, `metrics.zip` includes 10 code change metrics used in commit type classification.

3. `empirical_study`: this is the result of empirical study abour the correlation between code change situations and 8 commit types.

4. `experiment_results`: this is all original experiment results of Kevin and six state-of-the-art approaches.

    1. `RQ1 Overall Effectiveness`: this is for experiment RQ1.

    2. `RQ2 Ablation Study`: this is for experiment RQ2.

    3. `RQ3 Human Evaluation`: this is for experiment RQ3.

    4. `metrics`: this is the implement of automated evaluation metrics BLEU, ROUGE-L, and METEOR.

5. `checkpoints`: this directory includes five checkpoints of commit description generation model across five languages.


# Usage


Before run the model, unzip all dataset in directory `dataset/`.


``` shell

unzip MCMD_filter.zip

unzip metrics.zip

```


In the directory `code/commit_type_classification` and `code/commit_description_generation`, there are detailed document for install dependencies and run the model.


To run commit type classification model,


``` shell

python classification.py

```


If extract features from original dataset, configure parameters in function `main()` of `code/commit_type_classification/get_feature.py`, and

``` shell

python get_feature.py

python classification.py

```


To run commit description generation model, prepare the data modify the parameters in function `main()`.

``` shell

python prepare.py

```


configure parmeters in directory `code/commit_description_generation/conf/*.py`, includes the path of dataset, model, and hyperparameters.

``` shell

CUDA_VISIBLE_DEVICES=0 python eval.py +model=codet5

```


If train the model,

``` shell

CUDA_VISIBLE_DEVICES=0 python train.py +model=codet5

```
DOI: 10.5281/zenodo.12909210
title: Kevin: Knowledge-Enhanced Conventional Commit Message Generation

description: Introduction


We purpose a novel conventional commit message generation framework Kevin. In this directory, we give all code, dataset, and experiment results.


We use the dataset MCMD to evaluate our model, and filter some meaningless data on [`it`](dataset/MCMD_filter.zip).


Now we introduce the purpose of each folder:


1. `code`: this is the implement of our approach.

    1. `commit_type_classification`: this is implement of our approach in commit type classification, and there are detailed documents in this folder.

    2. `commit_description_classification`: this is implement of our approach in commit description generation, and there are detailed documents in this folder.

2. `dataset`: this is the dataset we used to evaluate our approach, `MCMD_filter.zip` contains the filtered version of the dataset MCDM, `metrics.zip` includes 10 code change metrics used in commit type classification.

3. `empirical_study`: this is the result of empirical study abour the correlation between code change situations and 8 commit types.

4. `experiment_results`: this is all original experiment results of Kevin and six state-of-the-art approaches.

    1. `RQ1 Overall Effectiveness`: this is for experiment RQ1.

    2. `RQ2 Ablation Study`: this is for experiment RQ2.

    3. `RQ3 Human Evaluation`: this is for experiment RQ3.

    4. `metrics`: this is the implement of automated evaluation metrics BLEU, ROUGE-L, and METEOR.

5. `checkpoints`: this directory includes five checkpoints of commit description generation model across five languages.


# Usage


Before run the model, unzip all dataset in directory `dataset/`.


``` shell

unzip MCMD_filter.zip

unzip metrics.zip

```


In the directory `code/commit_type_classification` and `code/commit_description_generation`, there are detailed document for install dependencies and run the model.


To run commit type classification model,


``` shell

python classification.py

```


If extract features from original dataset, configure parameters in function `main()` of `code/commit_type_classification/get_feature.py`, and

``` shell

python get_feature.py

python classification.py

```


To run commit description generation model, prepare the data modify the parameters in function `main()`.

``` shell

python prepare.py

```


configure parmeters in directory `code/commit_description_generation/conf/*.py`, includes the path of dataset, model, and hyperparameters.

``` shell

CUDA_VISIBLE_DEVICES=0 python eval.py +model=codet5

```


If train the model,

``` shell

CUDA_VISIBLE_DEVICES=0 python train.py +model=codet5

```
DOI: 10.5281/zenodo.13188340
title: Kevin: Knowledge-Enhanced Conventional Commit Message Generation

description: Introduction


We purpose a novel conventional commit message generation framework Kevin. In this directory, we give all code, dataset, and experiment results.


We use the dataset MCMD to evaluate our model, and filter some meaningless data on [`it`](dataset/MCMD_filter.zip).


Now we introduce the purpose of each folder:


1. `code`: this is the implement of our approach.

    1. `commit_type_classification`: this is implement of our approach in commit type classification, and there are detailed documents in this folder.

    2. `commit_description_classification`: this is implement of our approach in commit description generation, and there are detailed documents in this folder.

2. `dataset`: this is the dataset we used to evaluate our approach, `MCMD_filter.zip` contains the filtered version of the dataset MCDM, `metrics.zip` includes 10 code change metrics used in commit type classification.

3. `empirical_study`: this is the result of empirical study abour the correlation between code change situations and 8 commit types.

4. `experiment_results`: this is all original experiment results of Kevin and six state-of-the-art approaches.

    1. `RQ1 Overall Effectiveness`: this is for experiment RQ1.

    2. `RQ2 Ablation Study`: this is for experiment RQ2.

    3. `RQ3 Human Evaluation`: this is for experiment RQ3.

    4. `metrics`: this is the implement of automated evaluation metrics BLEU, ROUGE-L, and METEOR.

5. `checkpoints`: this directory includes five checkpoints of commit description generation model across five languages.


# Usage


Before run the model, unzip all dataset in directory `dataset/`.


``` shell

unzip MCMD_filter.zip

unzip metrics.zip

```


In the directory `code/commit_type_classification` and `code/commit_description_generation`, there are detailed document for install dependencies and run the model.


To run commit type classification model,


``` shell

python classification.py

```


If extract features from original dataset, configure parameters in function `main()` of `code/commit_type_classification/get_feature.py`, and

``` shell

python get_feature.py

python classification.py

```


To run commit description generation model, prepare the data modify the parameters in function `main()`.

``` shell

python prepare.py

```


configure parmeters in directory `code/commit_description_generation/conf/*.py`, includes the path of dataset, model, and hyperparameters.

``` shell

CUDA_VISIBLE_DEVICES=0 python eval.py +model=codet5

```


If train the model,

``` shell

CUDA_VISIBLE_DEVICES=0 python train.py +model=codet5

```
DOI: 10.5281/zenodo.11523050
title: Kevin: Knowledge-Enhanced Conventional Commit Message Generation

description: Introduction


We purpose a novel conventional commit message generation framework Kevin. In this directory, we give all code, dataset, and experiment results.


We use the dataset MCMD to evaluate our model, and filter some meaningless data on [`it`](dataset/MCMD_filter.zip).


Now we introduce the purpose of each folder:


1. `code`: this is the implement of our approach.

    1. `commit_type_classification`: this is implement of our approach in commit type classification, and there are detailed documents in this folder.

    2. `commit_description_classification`: this is implement of our approach in commit description generation, and there are detailed documents in this folder.

2. `dataset`: this is the dataset we used to evaluate our approach, `MCMD_filter.zip` contains the filtered version of the dataset MCDM, `metrics.zip` includes 10 code change metrics used in commit type classification.

3. `empirical_study`: this is the result of empirical study abour the correlation between code change situations and 8 commit types.

4. `experiment_results`: this is all original experiment results of Kevin and six state-of-the-art approaches.

    1. `RQ1 Overall Effectiveness`: this is for experiment RQ1.

    2. `RQ2 Ablation Study`: this is for experiment RQ2.

    3. `RQ3 Human Evaluation`: this is for experiment RQ3.

    4. `metrics`: this is the implement of automated evaluation metrics BLEU, ROUGE-L, and METEOR.

5. `checkpoints`: this directory includes five checkpoints of commit description generation model across five languages.


# Usage


Before run the model, unzip all dataset in directory `dataset/`.


``` shell

unzip MCMD_filter.zip

unzip metrics.zip

```


In the directory `code/commit_type_classification` and `code/commit_description_generation`, there are detailed document for install dependencies and run the model.


To run commit type classification model,


``` shell

python classification.py

```


If extract features from original dataset, configure parameters in function `main()` of `code/commit_type_classification/get_feature.py`, and

``` shell

python get_feature.py

python classification.py

```


To run commit description generation model, prepare the data modify the parameters in function `main()`.

``` shell

python prepare.py

```


configure parmeters in directory `code/commit_description_generation/conf/*.py`, includes the path of dataset, model, and hyperparameters.

``` shell

CUDA_VISIBLE_DEVICES=0 python eval.py +model=codet5

```


If train the model,

``` shell

CUDA_VISIBLE_DEVICES=0 python train.py +model=codet5

```
DOI: 10.5281/zenodo.11523051
title: Kevin: Knowledge-Enhanced Conventional Commit Message Generation

description: This repository includes the code, data, and result of our approaches Kevin. 

MCMD_filter.zip: the dataset we used 

metrics.zip: the 10 code change metrics used in commit type classification

Kevin.zip: the code and experimental results
DOI: 10.6084/m9.figshare.26034574
title: Figshare20240613.pdf. Data obtained by American Heart Association Transformational Project Grant, 19TPA34850170.Update

description: Figure 1. Sirt1 is inactivated by HFD feeding. (A) Sirt1 at protein levels were examined after 1 month of HFD feeding in Sirt1cKO mice, and after 3 months of HFD feeding in wild type mice. (B) Sirt1 at protein level was examined in Sirt1cTG mice. (C) Expression of Sirt1 in 3 months of HFD feeding. (D) Decrease in NAD/NADH ratio by HFD feeding. After 1 month of HFD feeding, NAD and NADH levels were examined. (E-F) HFD-induced protein acetylation and acetylation of p65 at K310 are enhanced in Sirt1cKO (E) but inhibited in Sirt1cTG (F) mice. The numbers of mice examined in each experimental group were: 6 (A), 6-7 (B), 7(C), 8 (D), 7 (acetyl lysine, E), 6-8 (acetyl-RelA at K310, E), 5 (acetyl lysine, F) and 6-8 (acetyl-RelA at K310, E). Statistical significance was determined with Student t test (A and D) and ANOVA (B, E to F).Figure 2. Sirt1 negatively regulates HFD-induced diastolic dysfunction. (A) HFD-induced diastolic dysfunction is exacerbated in Sirt1cKO mice. (B-C) HFD or loss of Sirt1 does not significantly affect cardiac systolic function (B) and cardiac hypertrophy (C). (D) HFD-induced increase in body weight was not significantly changed Sirt1cKO mice. (E) HFD-induced diastolic dysfunction is ameliorated in Sirt1cTG mice. (F-G) HFD or gain of Sirt1 does not significantly affect cardiac systolic function (F) and cardiac hypertrophy (G). (H) HFD-induced increase in body weight was not significantly changed Sirt1cKO mice. PV loop analyses (A and E), echocardiographic measurement (B and F), and heart (C and G) and body weight (D and H) measurements were performed after 3 months of HFD feeding. The numbers of mice examined in each experimental group were: 6-10 (A), 7-8 (B), 14-20 (C and D), 5-8 (E), 7-13 (F), and 10-15 (G and H). Statistical significance was determined with ANOVA (A,B,C,D,E,F, and G) and Kruskall-Wallis (H).Figure 3. Exogenous Sirt1 expression with AAV inhibits HFD-induced diastolic dysfunction, fibrosis and TGFβ1 signaling. After 1 month of HFD feeding, AAV-Sirt1 or AAV-GFP (control) were injected. PV loop analyses (A) and PASR staining (B) were performed after 2 month of AAV injection. The numbers of mice examined in each experimental group were: 5 (A) and 8 (B). Statistical significance was determined with ANOVA.
DOI: 10.6084/m9.figshare.26034574.v2
title: Figshare20240613.pdf. Data obtained by American Heart Association Transformational Project Grant, 19TPA34850170.Update

description: Figure 1. Sirt1 is inactivated by HFD feeding. (A) Sirt1 at protein levels were examined after 1 month of HFD feeding in Sirt1cKO mice, and after 3 months of HFD feeding in wild type mice. (B) Sirt1 at protein level was examined in Sirt1cTG mice. (C) Expression of Sirt1 in 3 months of HFD feeding. (D) Decrease in NAD/NADH ratio by HFD feeding. After 1 month of HFD feeding, NAD and NADH levels were examined. (E-F) HFD-induced protein acetylation and acetylation of p65 at K310 are enhanced in Sirt1cKO (E) but inhibited in Sirt1cTG (F) mice. The numbers of mice examined in each experimental group were: 6 (A), 6-7 (B), 7(C), 8 (D), 7 (acetyl lysine, E), 6-8 (acetyl-RelA at K310, E), 5 (acetyl lysine, F) and 6-8 (acetyl-RelA at K310, E). Statistical significance was determined with Student t test (A and D) and ANOVA (B, E to F).Figure 2. Sirt1 negatively regulates HFD-induced diastolic dysfunction. (A) HFD-induced diastolic dysfunction is exacerbated in Sirt1cKO mice. (B-C) HFD or loss of Sirt1 does not significantly affect cardiac systolic function (B) and cardiac hypertrophy (C). (D) HFD-induced increase in body weight was not significantly changed Sirt1cKO mice. (E) HFD-induced diastolic dysfunction is ameliorated in Sirt1cTG mice. (F-G) HFD or gain of Sirt1 does not significantly affect cardiac systolic function (F) and cardiac hypertrophy (G). (H) HFD-induced increase in body weight was not significantly changed Sirt1cKO mice. PV loop analyses (A and E), echocardiographic measurement (B and F), and heart (C and G) and body weight (D and H) measurements were performed after 3 months of HFD feeding. The numbers of mice examined in each experimental group were: 6-10 (A), 7-8 (B), 14-20 (C and D), 5-8 (E), 7-13 (F), and 10-15 (G and H). Statistical significance was determined with ANOVA (A,B,C,D,E,F, and G) and Kruskall-Wallis (H).Figure 3. Exogenous Sirt1 expression with AAV inhibits HFD-induced diastolic dysfunction, fibrosis and TGFβ1 signaling. After 1 month of HFD feeding, AAV-Sirt1 or AAV-GFP (control) were injected. PV loop analyses (A) and PASR staining (B) were performed after 2 month of AAV injection. The numbers of mice examined in each experimental group were: 5 (A) and 8 (B). Statistical significance was determined with ANOVA.
DOI: 10.15468/dl.jt25b2
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Wyethia ×magna A.Nelson"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014046-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.20509113
title: FMGM_synthetic_data_example.zip

description: Simulated 2-group mixed data for fused Markov random field (FMGM) method, and an example script for analysis. Data contains 500 observations (250 for each group) for 100 variables (50 Gaussian & 50 4-level categorial). Data was generated with Markov chain Monte Carlo with fully conditional distributions based on networks generated with power-law network structures.
DOI: 10.6084/m9.figshare.20509113.v3
title: FMGM_synthetic_data_example.zip

description: Simulated 2-group mixed data for fused Markov random field (FMGM) method, and an example script for analysis. Data contains 500 observations (250 for each group) for 100 variables (50 Gaussian & 50 4-level categorial). Data was generated with Markov chain Monte Carlo with fully conditional distributions based on networks generated with power-law network structures.
DOI: 10.6084/m9.figshare.20509113.v2
title: FMGM_synthetic_data_example.zip

description: Simulated 2-group mixed data for fused Markov random field (FMGM) method, and an example script for analysis. Data contains 500 observations (250 for each group) for 100 variables (50 Gaussian & 50 4-level categorial). Data was generated with Markov chain Monte Carlo with fully conditional distributions based on networks generated with power-law network structures.
DOI: 10.15468/dl.d23j8g
title: Occurrence Download

description: A dataset containing 951227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Material sample, Specimen, Living Specimen, Human Observation, Occurrence evidence)",
 "Geometry POLYGON((-2.87581 54.28868,-0.20359 54.28868,-0.20359 55.67985,-2.87581 55.67985,-2.87581 54.28868))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 951227 records from 112 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014025-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bhnjg2
title: Occurrence Download

description: A dataset containing 1673 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1990",
 "TaxonKey is Aphyllon fasciculatum (Nutt.) Torr. & A.Gray"
 ]
}

 The dataset includes 1673 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014047-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dkf9rx
title: Occurrence Download

description: A dataset containing 19072 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oophaga Bauer, 1994"
 ]
}

 The dataset includes 19072 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014048-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ug53fh
title: Occurrence Download

description: A dataset containing 1673 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1990",
 "TaxonKey is Aphyllon fasciculatum (Nutt.) Torr. & A.Gray"
 ]
}

 The dataset includes 1673 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014049-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rd2aty
title: Occurrence Download

description: A dataset containing 526 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1990",
 "TaxonKey is Atriplex argentea Nutt."
 ]
}

 The dataset includes 526 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014050-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qwz42f
title: Occurrence Download

description: A dataset containing 1673 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1990",
 "TaxonKey is Aphyllon fasciculatum (Nutt.) Torr. & A.Gray"
 ]
}

 The dataset includes 1673 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014051-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wepee9
title: Occurrence Download

description: A dataset containing 48106 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cariama cristata (Linnaeus, 1766)",
 "Year 2000-2024"
 ]
}

 The dataset includes 48106 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014052-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gxwq9r
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is A new species of Conocephalus (Orthoptera: Tettigoniidae: Conocephalinae) from Argentina with extraordinarily broad-band ultrasound calling song"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014053-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hf79me
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abronia martindelcampoi Flores-Villela & Sánchez-H., 2003"
 ]
}

 The dataset includes 38 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014054-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w8tsxt
title: Occurrence Download

description: A dataset containing 617 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Passiflora L.",
 "Year 2020-2024"
 ]
}

 The dataset includes 617 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014055-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nu48ug
title: Occurrence Download

description: A dataset containing 877761 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lycopodiaceae"
 ]
}

 The dataset includes 877761 records from 1865 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013981-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.