This document is included in the 'Recommending Move Method Refactoring Opportunities using Structural and Semantic Representations of Code' distribution，which we will refer to as RMove，This is to distinguish the recommended implementation of this move method refactoring from other implementations.https://github.com/wangsiqidahaoren/Rmove-method In this document, the environment required to make and use the Rmove tool is described. Some hints about the installation environment are here, but users need to find complete instructions from other sources. They give a more detailed description of their tools and instructions for using them. My main environment is located on a computer with windows (windows 10 at the time of my writing) operating system. The fundamentals should be similar for other platforms, although the way in which the environment is configured will be different. What do I mean by environment? For example, to run python code you will need to install a python interpreter, and if you want to use code2vec you will need tensorflow.
/json : Project dependency using depends
/moved_method : Recommended method of moving
/src : The code files which is involved in the experiment
/Training_CSV_code2seq : The training data of feature fusion with code2seq and network embedding
/Training_CSV_code2vec : The training data of feature fusion with code2vec and network embedding
code2vec code2seq
DeepWalk LINE Node2vec GraRep SDNE ProNE walklets
Python3 (>=3.6). To check the version:
TensorFlow - version 2.0.0 (install). To check TensorFlow version:
If you are using a GPU, you will need CUDA 10.0 (download) as this is the version that is currently supported by TensorFlow. To check CUDA version:
nvcc --version
For GPU: cuDNN (>=7.5) (download) To check cuDNN version:
Python3 (>=3.6). To check the version:
TensorFlow - version 2.0.0 (install). To check TensorFlow version:
If you are using a GPU, you will need CUDA 10.0 (download) as this is the version that is currently supported by TensorFlow. To check CUDA version:
nvcc --version
For GPU: cuDNN (>=7.5) (download) To check cuDNN version:
They all come from the open source project OpenNE
numpy==1.14 networkx==2.0 scipy==0.19.1 tensorflow>=1.12.1 gensim==3.0.1 scikit-learn==0.19.0
numpy sklearn networkx gensim
tqdm 4.28.1 numpy 1.15.4 pandas 0.23.4 texttable 1.5.0 gensim 3.6.0 networkx 2.4
step1:Use code2vec and code2seq to embedding the semantic information of the code and get the vectors corresponding to the methods and classes
eg: feature1 feature2 feature3 ...... featuren 0.98 0.16 0.98 ....... 0.87
step 2:We use graph embedding networks such as DeepWalk to embedding the structural information of the code dependencies and get the structural vector of the method dependency graph
eg: feature1 feature2 feature3 ...... featuren 0.98 0.16 0.98 ....... 0.87
step3 : We train the training set using classifiers commonly used in machine learning and deep learning, and optimize the hyperparameters using grid search
step4 : Model evaluation on the real-world dataset
train data PMD Cayenne Pinpoint Jenkins Drools
real world data Weka Ant FreeCol JMeter FreeMind JTOpen DrJava Maven
We generate training data from a small set of detected move method detection results. the paper presents the procedure of training data generation.The input of this algorithm is a set of detected move method results: MoveMethodSet. This algorithm inspects each item in MoveMethodSet iteratively.