A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, a great many numbers of code clone detection techniques and tools have been proposed and studied over the last decade such as, NiCAD [2], Cloneworks [3], SourcererCC [4] and so on. To detect all possible similar source code patterns in general, the clone detection tools work on syntax level (such as texts, tokens, AST and so on) while lacking user-specific preferences. This often means the reported clones must be manually validated prior to any analysis in order to filter out the true positive clones from task or user-specific considerations. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection.
This is a machine learning based framework for automatic code clone validation - developed based on our recent research study [1]. The method learns to predict tasks or user-specific code clone validation patterns. The current machine learning model has been build based on BigCloneBench [5] - a collection of eight million validated clones within IJaDataset-2.0, a big data software repository containing 25,000 open-source Java systems. In addition to the useability of the trained model locally for code clone classification, this cloud based framework also supports the communication with any existing code clone detection tools for valdiation prediction responses using REST API. Please refer to the paper for additional details of the framework [1]. Following is a high-level architecutre of CloneCognition.
Please make sure you have the following environment setups:
-
Python 2.7 (This framework has been developed using Python 2.7.6).
-
Pybrain (This project used Pybrain (http://pybrain.org/) for building the neural network model).
-
Pickle (For loading the pickled neural network model from Pybrain)
On Cloning and setting up the required environment for this project, you need to follow the speps below:
$cd CloneCognition
The framework works on a set of clone files (output of a code clone detection tools) for validation. The used xml format for parsing the clone pairs are as follows. All the detected clone pairs - clone, should have to be children of the root tag - clones. A clone contains details information (e.g., in source tag) and codes (e.g., in code tag) for both of its clone fragments. Copy all such clone files in a directory for starting the validation (e.g., as next step).
<clones>
<clone>
<source file= "selected/1966294.java" startline= "168" endline= "181" />
<code>
//clone fragment 1
</code>
<source file= "selected/1966294.java" startline= "58" endline= "65" />
<code>
//clone fragment 2
</code>
</clone>
...
...
<clones>
$python validateClones.py <Validation Threshold> <Input Directory> <Output Directory>
Where,
- Validation Threshold : The preffered threshold value (e.g., prob. [0,1]) for deciding a potential clone pair to be true.
- Input Directory: The directory of all detected clone files formatted as above (e.g., as shown in step 2).
- Output Directory: The destination directory to write the validation responses.
So, an example usage would be:
$python validateClones.py 0.6 DetectedSystemClonesDir/ ML_ValidationResponse/
The framework creats output file containing validation information for each of the clone files. The extensions of the output files are - .mlValidated, which can be loaded as csv formats for further analysis of the validation results. The validation response (e.g., true/false) for each of the clone pairs are as follows. You will get overall validation statistics (e.g., precision and so on) in your console and will also be written in __CLONE_VALIDATION_STATS.txt file in your specified output directory (e.g., in ).
validation_response,fragment_1_path,fragment_1_startline,fragment_1_endline,fragment_2_path,fragment_2_startline,fragment_2_endline
For testing if everything has been set up accordingly, you can run the validation on a provided clone file with this framework. The sample clone pairs are available in input_clone_pairs directory. So, you can run the following command to test the successfull installation of the framework. If evererythin works fine, you should get validation statistics (e.g, precision, TP clones and so on) on your console. The validation statistics will also be available in __CLONE_VALIDATION_STATS.txt file in Out/ directory.
$python validateClones.py 0.5 input_clone_pairs/ out/
The following figure illustrates automatic validation report presented in from user interface in addition to textual report:
Get the new BigCloneBench2.1 from here https://drive.google.com/open?id=1v5LDVWXGbfN4a1oXvrwcGaXtX46uDR-4
For training on new dataset you can use ANN_CloneValidator(https://github.com/pseudoPixels/ANN_CloneValidator) repository. Use the training script (https://github.com/pseudoPixels/ANN_CloneValidator/blob/master/clone_validator.py) for the new trained model. The script stores the learned model in a pickle file which can be plugged in for automatic clone validation as instruction shown in this readme document.
Use the script (resultAnalysis2.py, resultAnalysis3.py, resultAnalysis4.py) from ANN_CloneValidator (https://github.com/pseudoPixels/ANN_CloneValidator) for different visualization of the obtained results.
Please add your issues or bug reports to this git repository. We track the issues for further improvement of the framework.
[1] Mostaeen, G., Svajlenko, J., Roy, B., Roy, C. K., & Schneider, K. (2018, September). On the Use of Machine Learning Techniques Towards the Design of Cloud Based Automatic Code Clone Validation Tools. In Source Code Analysis and Manipulation (SCAM), 2018 IEEE 18th International Working Conference on. IEEE.
[2] Roy, C. K., & Cordy, J. R. (2008, June). NICAD: Accurate detection of near-miss intentional clones using flexible pretty-printing and code normalization. In Program Comprehension, 2008. ICPC 2008. The 16th IEEE International Conference on (pp. 172-181). IEEE.
[3] Svajlenko, J., & Roy, C. K. (2017, May). Cloneworks: A fast and flexible large-scale near-miss clone detection tool. In Proceedings of the 39th International Conference on Software Engineering Companion (pp. 177-179). IEEE Press.
[4] Sajnani, H., Saini, V., Svajlenko, J., Roy, C. K., & Lopes, C. V. (2016, May). SourcererCC: scaling code clone detection to big-code. In Software Engineering (ICSE), 2016 IEEE/ACM 38th International Conference on (pp. 1157-1168). IEEE.
[5] Svajlenko, J., & Roy, C. K. (2015, September). Evaluating clone detection tools with bigclonebench. In Software Maintenance and Evolution (ICSME), 2015 IEEE International Conference on (pp. 131-140). IEEE.
[6] Ambient Software Evoluton Group. IJaDataset 2.0. http://secold.org/projects/seclone.