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Network-clustered Multi-modal Bug Localization.

Building the NetML algorithm, the figure belows illustrate our proposed model:

Propose model for the NetML

First time setup

Please install the neccessary libraries before running our NetML model:

  • python 2.7

  • numpy 1.13.1

  • scikit-learn 0.19.0

Dataset details

  • The dataset is put in two folders ./data and ./data_defect4j. The folder ./data includes four projects, namely Ant, Aspectj, Lucene, and Rhino, whereas the folder ./data_defect4j includes three projects, namely Lang, Math, and Time.

Example running

  • Please run test_NetML.py to get used to NetML model. We see that the loss value descrease which means that our loss function converges.

Input data

  • All the bug reports for each project.

  • All the methods for each project.

  • Features for each pair of bug report-method, the features mention whether a relationship between the bug report and method.

  • Label data

  • Please take a look at folder ./data_example to see the input of our framework:

    • bug_list.txt: all the bug reports
    • method_list.txt: all the methods
    • features.txt: features for each pair of bug-method
    • groundtruth.txt: label data

Parameters:

We have five different parameters:

  • nfolds: number of folds to do cross-validation
  • iters: number of iterations for training NetML
  • alpha and beta: control the strength of ridge and Network lasso regularization, respectively.
  • kNN: number of nearest of neighbors

Running the model

Simply run this command to train the network:

$ python run_NetML.py ./data_example/bug_list.txt ./data_example/method_list.txt ./data_example/features.txt ./data_example/groundtruth.txt 10 30 0.1 0.01 10

Note that in this case, nfolds=10, iters=30, alpha=0.1, beta=0.01, and kNN=10

Example output:

Output

Contact:

If you have any questions, please send the email to: vdthoang.2016@smu.edu.sg