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golivax authored Nov 17, 2017
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Patanamon Thongtanunam, Shane McIntosh, Ahmed E. Hassan, and Hajimu Iida
[Empirical Software Engineering Journal, vol. 22, issue 2, 2017](https://doi.org/10.1007/s10664-016-9452-6)

## Bibtex
## 1) Bibtex

```bibtex
@article{Thongtanunam2017,
Expand All @@ -27,64 +27,64 @@ Patanamon Thongtanunam, Shane McIntosh, Ahmed E. Hassan, and Hajimu Iida
}
```

## Download Processed Datasets
## 2) Download Processed Datasets

Each dataset contains the 20 patch and MCR metrics for the studied patches. Each row also includes review ID, number of reviewers, discussion length, date of last update before feedback, and date of the first feedback of a patch.

- Android dataset (csv file, ~11 MB)
- Qt dataset (csv file, ~23 MB)
- OpenStack dataset (csv file, ~33 MB)

You can download the datasets [here]()
You can download these datasets [here](https://github.com/SAILResearch/replication-participation_code_review/releases/latest)

## Additional Results
## 3) Additional Results

### RQ1 Results
### 3.1) RQ1 Results

Analysis | Android | QT | OpenStack
-------------------------------------------|-----------------------------|-----------------------------|--------------------
Model Construction | Variable Clustering | Variable Clustering | Variable Clustering
" | Spearman multiple rho^2 | Spearman multiple rho^2 | Spearman multiple rho^2
Nonlinear relationship with the likelihood | #Reviewers of prior patches | #Reviewers of prior patches | #Reviewers of prior patches
" | - | Description length | -
Analysis | Android | QT | OpenStack
-------------------------------------------|-------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------
Model Construction | [Variable Clustering](figures/varclus/varclus_NumberOfReviewers_AndroidReviews.pdf) | [Variable Clustering](figures/varclus/varclus_NumberOfReviewers_QtReviews.pdf) | [Variable Clustering](figures/varclus/varclus_NumberOfReviewers_OpenStackReviews.pdf)
" | [Spearman multiple rho^2](figures/spearman/estimateDF_NumberOfReviewers_AndroidReviews.pdf) | [Spearman multiple rho^2](figures/spearman/estimateDF_NumberOfReviewers_QtReviews.pdf) | [Spearman multiple rho^2](figures/spearman/estimateDF_NumberOfReviewers_OpenStackReviews.pdf)
Nonlinear relationship with the likelihood | [#Reviewers of prior patches](figures/rq1/results_NumberOfReviewers_Android_PriorReviewers.pdf) | [#Reviewers of prior patches](figures/rq1/results_NumberOfReviewers_Qt_PriorReviewers.pdf) | [#Reviewers of prior patches](figures/rq1/results_NumberOfReviewers_OpenStack_PriorReviewers.pdf)
" | - | [Description length](figures/rq1/results_NumberOfReviewers_Qt_DescriptionLength.pdf) | -

### RQ2 Results
### 3.2) RQ2 Results

Analysis | Android | QT | OpenStack
-------------------------------------------|------------------------------------|------------------------------------|--------------------
Model Construction | Variable Clustering | Variable Clustering | Variable Clustering
" | Spearman multiple rho^2 | Spearman multiple rho^2 | Spearman multiple rho^2
Nonlinear relationship with the likelihood | Discussion length of prior patches | Discussion length of prior patches | Discussion length of prior patches
" | Churn | Churn | -
Analysis | Android | QT | OpenStack
-------------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------------------
Model Construction | [Variable Clustering](figures/varclus/varclus_DiscussionLength_AndroidReviews.pdf) | [Variable Clustering](figures/varclus/varclus_DiscussionLength_QtReviews.pdf) | [Variable Clustering](figures/varclus/varclus_DiscussionLength_OpenStackReviews.pdf)
" | [Spearman multiple rho^2](figures/spearman/estimateDF_DiscussionLength_AndroidReviews.pdf) | [Spearman multiple rho^2](figures/spearman/estimateDF_DiscussionLength_QtReviews.pdf) | [Spearman multiple rho^2](figures/spearman/estimateDF_DiscussionLength_OpenStackReviews.pdf)
Nonlinear relationship with the likelihood | [Discussion length of prior patches](figures/rq2/results_DiscussionLength_Android_PriorDiscussionLength.pdf) | [Discussion length of prior patches](figures/rq2/results_DiscussionLength_Qt_PriorDiscussionLength.pdf) | [Discussion length of prior patches](figures/rq2/results_DiscussionLength_OpenStack_PriorDiscussionLength.pdf)
" | [Churn](figures/rq2/results_DiscussionLength_Android_Churn.pdf) | [Churn](figures/rq2/results_DiscussionLength_Qt_Churn.pdf) | -

### RQ3 Results
### 3.3) RQ3 Results

Analysis | Android | QT | OpenStack
-------------------------------------------|---------------------------------|---------------------------------|--------------------
Model Construction | Variable Clustering | Variable Clustering | Variable Clustering
" | Spearman multiple rho^2 | Spearman multiple rho^2 | Spearman multiple rho^2
Nonlinear relationship with the likelihood | Feedback delay of prior patches | Feedback delay of prior patches | Feedback delay of prior patches
" | - | - | Churn
Analysis | Android | QT | OpenStack
-------------------------------------------|-----------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|--------------------
Model Construction | [Variable Clustering](figures/varclus/varclus_FeedbackDelay_AndroidReviews.pdf) | [Variable Clustering](figures/varclus/varclus_FeedbackDelay_QtReviews.pdf) | [Variable Clustering](figures/varclus/varclus_FeedbackDelay_OpenStackReviews.pdf)
" | [Spearman multiple rho^2](figures/spearman/estimateDF_FeedbackDelay_AndroidReviews.pdf) | [Spearman multiple rho^2](figures/spearman/estimateDF_FeedbackDelay_QtReviews.pdf) | [Spearman multiple rho^2](figures/spearman/estimateDF_FeedbackDelay_OpenStackReviews.pdf)
Nonlinear relationship with the likelihood | [Feedback delay of prior patches](figures/rq3/results_FeedbackDelay_Android_PriorFeedbackDelay.pdf) | [Feedback delay of prior patches](figures/rq3/results_FeedbackDelay_Qt_PriorFeedbackDelay.pdf) | [Feedback delay of prior patches](figures/rq3/results_FeedbackDelay_OpenStack_PriorFeedbackDelay.pdf)
" | - | - | [Churn](figures/rq3/results_FeedbackDelay_OpenStack_Churn.pdf)

### Purpose Classification Validation Results
### 3.4) Purpose Classification Validation Results

We validate the purpose classification by taking a sample of 50 patches for each type of purpose. Then, we manually determine whether the patches are correctly classified or not.

- Android dataset (csv file, 10 KB)
- Qt dataset (csv file, 9 KB)
- OpenStack dataset (csv file, 9 KB)

This validation can download [here]()
This validation dataset can be downloaded [here](https://github.com/SAILResearch/replication-participation_code_review/releases/latest)

## Example R Scripts
## 4) Example R Scripts

### Install Packages
### 4.1) Install Packages

```R
install.packages("rms")
```

### Model Construction
### 4.2) Model Construction

This section shows how our model construction step is implemented. These R scripts are for our RQ1 with example outputs of Android dataset.

Expand Down Expand Up @@ -148,6 +148,7 @@ plot(vc)
threshold <- 0.7
abline(h=1-threshold, col = "red", lty = 2)
```
![](figures/used-in-readme/corr-1.png)

Remove the highly correlated variables from ```ind_vars``` vector and check for highly correlated variable again

Expand All @@ -167,6 +168,8 @@ threshold <- 0.7
abline(h=1-threshold, col = "red", lty = 2)
```

![](figures/used-in-readme/corr-2.png)

```R
#Churn and NumberOfFiles are still highly correlated. Therefore, we remove the NumberOfFiles variable out.
reject_vars <- c('NumberOfFiles')
Expand Down Expand Up @@ -265,6 +268,8 @@ sp <- spearman2(formula(paste("y" ," ~ ",paste0(ind_vars, collapse=" + "))), dat
plot(sp)
```

![](figures/used-in-readme/spearman.png)

#### (MC-3) Logistic regression model construction step fits a model to the data and uses ```rcs``` function to allocate additional degrees of freedom to the survinving variables in the ```ind_vars``` vector according to their Spearman multiple rho^2 values

```R
Expand All @@ -273,7 +278,7 @@ DaysSinceTheLastModification + DescriptionLength + NumberOfPriorDefects + Direct
PriorPatchesOfAnAuthor + Purpose + Churn + Entropy + OverallWorkload, data=data, x=T, y=T)
```

### Model Analysis
### 4.3) Model Analysis

This section shows how our model analysis step is implemented.

Expand Down Expand Up @@ -308,6 +313,8 @@ predict <- Predict(fit,NumberOfReviewersOfPriorPatches,fun=function(x)exp(x))
plot(predict, ylab='Odds')
```

![](figures/used-in-readme/response-var.png)

Estimate the partial effect

```R
Expand Down

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