ClonesInDLCode

Replication package of "Clones in Deep Learning Code: What, Where, and Why?"

This repository contains all the data, scripts, and source codes needed to replicate our study

Requirements

• Nicad 5.2
• Jupyter Notebook (Anaconda 3)
• Python 3.7.4

Folder Description

Our project contains the following folder, sub-folders, and files.

Research Questions

We have Jupyter notebooks by research questions

• RQ_1: Do code clones occur more frequently in deep learning code than traditional source code?
• RQ_2: How are code clones distributed in deep learning code in comparison to traditional source code?
• RQ_3: Do cloned and non-cloned code suffer similarly from bug-proneness in deep learning projects?
• RQ_4: Why do deep learning developers clone code?
• RQ_5: Which types of code cloning in deep learning clones are riskier?

Shell scripts folder

In this folder, we have all the scripts shell needed to clone repositories, to get GitHub repositories' informations, and to run NiCad.

Data folder

Data folder contains the followings:

• dlRepos.csv contains 59 deep learning repositories used in our study.
• tradRepos.csv contains 59 deep learning repositories used as a comparative data in our study.
• 6DLReposData.csv contains the selected 6 deep learning repositories to be manually analyzed.

Manual Analysis

This folder contains the results of the manual classification of the selected 6 repositories commits.

Clone Data

The results of clones by clone type (Type 1, Type 2, and Type 3) and by project type (DL: deep learning, Trad: traditional). In addition to bug fix and bug inducing commits data

contact: hadhemi.jebnoun@polymtl.ca