Published August 2, 2021
| Version v1.0.0
Dataset
Open
Replication package for the paper "Do Comments follow Commenting Conventions? A case study in Java and Python"
Description
# RP-comment-convention-adherence-Java-Python
Replication Package for the paper "Do Comments follow Commenting Conventions? A case study in Java and Python"
## Structure
```
RQ1/
RQ1_Java_Rules.xlsx
RQ1_Python_Rules.xlsx
RQ2/
RQ1_Java_Comments_Validated.xlsx
RQ1_Python_Comments_Validated.xlsx
```
## Contents of the Replication Package
---
- **RQ1/** - contains the data used to answer RQ1
- `RQ1_Java_Rules.xlsx` - contains comment-related rules extracted from various Java style guidelines. Various tabs in the sheet represent the rules extracted from standard or project-specific guidelines.
Oracle and Google are the standard guidelines, and the remaining are specific to the projects.
- `RQ1_Python_Rules.xlsx` - contains comment-related rules extracted from various Python style guidelines. Various tabs in the sheet represent the rules extracted from standard or project-specific guidelines. PEP, Numpy, and Google are the standard guidelines and the remaining are specific to the projects.
- **RQ2/** - contains the data used to answer RQ2
- `RQ2_Java_Comments_Validated.xlsx` - contains Java comment dataset used from the previous work and validated against the rules from their corresponding guidelines. Various tabs in the sheet represent various Java projects used in the work. The rows in each tab show the sample class comments used to validate against the rules. The rules are shown in the columns.
- `RQ2_Python_Comments_Validated.xlsx` - contains Python comment dataset used from the previous work and validated against the rules from their corresponding guidelines. Various tabs in the sheet represent various Java projects used in the work. The rows in each tab show the sample class comments used to validate against the rules. The rules are shown in the columns.
---
Files
SCAM-do-comments-follow-conventions.zip
Files
(3.1 MB)
Name | Size | Download all |
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md5:dfebc37e095ccd16d316367244d1907e
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3.1 MB | Preview Download |