Implement the process of bug localization.
This commit is contained in:
Kui LIU
@@ -0,0 +1,47 @@
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Step 1:
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Prepare data for tokens embedding of edit scripts.
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Input data: parsed results of patches with GumTree.
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Select token vectors of edit scripts by the value of upper whisker.
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Step 2:
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Embed tokens of all selected edit scripts.
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Step 3:
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Prepare data for features learning of selected edit scripts.
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Vectorize edit scripts with embedded tokens of edit scripts.
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Step 4:
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Learn features of all selected edit scripts with CNN algorithm.
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Input data: vectorized edit scripts.
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Step 5:
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Prepare data for clustering of edit scripts.
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Input data: learned features of edit scripts by CNN.
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Step 6:
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Clustering of edit scripts with extracted features of edit scripts.
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Step 7:
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Analyze cluster results to obtain common fix patterns.
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Step 8:
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Prepare testing data for evaluation.
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Parse java projects to get the token vectors of all statements.
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Step 9:
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Prepare data for evaluation.
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Merge token vectors of source code of training data and testing data.
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Step 10:
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Prepare data for evaluation.
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Embed tokens of source code vectors of training data and testing data.
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Step 11:
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Prepare data for evaluation.
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Vectorize data (token vectors of source code) for deep learning.
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Step 12:
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Evaluation: extract features of testing data and predict their labels.
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Step 13:
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@@ -1,13 +1,23 @@
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package edu.lu.uni.serval.FixPatternMining.App;
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import java.io.BufferedReader;
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import java.io.File;
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import java.io.FileInputStream;
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import java.io.FileNotFoundException;
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import java.io.IOException;
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import java.io.StringReader;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Scanner;
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import edu.lu.uni.serval.FixPatternMining.DataPrepare.DataPreparation;
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import edu.lu.uni.serval.config.Configuration;
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import edu.lu.uni.serval.utils.FileHelper;
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/**
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* Evaluation: extract features of testing data and predict their labels.
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* Separate features of training data cluster by cluster.
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*
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* Classify testing data by possibilities: 90%, 80%, 70%, and 60%, ignore others.
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*
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* @author kui.liu
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*
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@@ -17,17 +27,139 @@ public class Step13 {
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public static void main(String[] args) {
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boolean isSupervisedLearning = true;
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if (isSupervisedLearning) {// supervised learning
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// localization file, feature file,
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// label --> possibility --> 90, 80, 70, 60 others ignored, level one localization
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// features of training data
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DataPreparation.separateTrainingDataFeatures();
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String positionFiles = Configuration.TEST_POSITION_FILE;
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String featureFiles = Configuration.FEATURES_OF_TESTING_DATA;
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String labelFiles = Configuration.PREDICTED_RESULTS_OF_TESTING_DATA;
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String possibilitiesFilePath = Configuration.POSSIBILITIES_OF_TESTING_DATA;
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List<File> possibilitiesFiles = FileHelper.getAllFilesInCurrentDiectory(possibilitiesFilePath, ".csv");
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String bugs90 = Configuration.TESTING_DATA_BUGS90;
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String bugs80 = Configuration.TESTING_DATA_BUGS80;
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String bugs70 = Configuration.TESTING_DATA_BUGS70;
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String bugs60 = Configuration.TESTING_DATA_BUGS60;
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StringBuilder builder90 = new StringBuilder();
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int counter90 = 0;
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StringBuilder builder80 = new StringBuilder();
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int counter80 = 0;
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StringBuilder builder70 = new StringBuilder();
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int counter70 = 0;
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StringBuilder builder60 = new StringBuilder();
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int counter60 = 0;
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for (File possibilitiesFile : possibilitiesFiles) {
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String fileName = possibilitiesFile.getName();
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String positionFile = positionFiles + "Positions" + fileName.substring(fileName.lastIndexOf("_"), fileName.lastIndexOf(".")) + ".list";
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String featureFile = featureFiles + fileName;
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String labelFile = labelFiles + fileName;
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List<String> possibilities = readData(fileName);
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List<String> positions = readData(positionFile);
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List<String> features = readData(featureFile);
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List<Integer> labels = readLabel(labelFile);
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for (int index = 0, size = possibilities.size(); index < size; index ++) {
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String possibilityStr = possibilities.get(index);
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String[] array = possibilityStr.split(", ");
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int label = labels.get(index);
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double possibility = Double.parseDouble(array[label]);
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String position = positions.get(index);
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String feature = features.get(index);
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// And Label
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if (possibility >= 0.9) {
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builder90.append("LABEL:" + label + "Feature:" + feature + "Position:" + position + "\n");
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counter90 ++;
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if (counter90 % 1000 == 0) {
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FileHelper.outputToFile(bugs90, builder90, true);
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builder90.setLength(0);
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}
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} else if (possibility >= 0.8) {
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builder80.append("LABEL:" + label + "Feature:" + feature + "Position:" + position + "\n");
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counter80 ++;
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if (counter80 % 1000 == 0) {
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FileHelper.outputToFile(bugs80, builder80, true);
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builder80.setLength(0);
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}
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} else if (possibility >= 0.7) {
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builder70.append("LABEL:" + label + "Feature:" + feature + "Position:" + position + "\n");
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counter70 ++;
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if (counter70 % 1000 == 0) {
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FileHelper.outputToFile(bugs70, builder70, true);
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builder70.setLength(0);
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}
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} else if (possibility >= 0.6) {
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builder60.append("LABEL:" + label + "Feature:" + feature + "Position:" + position + "\n");
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counter60 ++;
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if (counter60 % 1000 == 0) {
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FileHelper.outputToFile(bugs60, builder60, true);
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builder60.setLength(0);
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}
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}
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}
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}
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FileHelper.outputToFile(bugs90, builder90, true);
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FileHelper.outputToFile(bugs80, builder80, true);
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FileHelper.outputToFile(bugs70, builder70, true);
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FileHelper.outputToFile(bugs60, builder60, true);
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// label: clusterNum, re-compute similarity with each element. 90, 80, 70, 60.
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// similarity: patches --> fixing bug.
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List<File> testingDataFiles = FileHelper.getAllFilesInCurrentDiectory(Configuration.TESTING_DATA, ".csv");
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for (int i = 0, size = testingDataFiles.size(); i < size; i ++) {
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}
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} else { // un-supervised learning
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// Extracted Features: Configuration.EXTRACTED_FEATURES_TESTING;
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// Compute the similarity: cosin similarity
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}
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}
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private static List<Integer> readLabel(String labelFile) {
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List<Integer> labels = new ArrayList<>();
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String fileContent = FileHelper.readFile(labelFile);
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BufferedReader reader = null;
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try {
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reader = new BufferedReader(new StringReader(fileContent));
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String line = null;
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while ((line = reader.readLine()) != null) {
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String[] labelsStr = line.split(", ");
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for (int i = 0, length = labelsStr.length; i < length; i ++) {
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Double d = Double.parseDouble(labelsStr[i]);
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labels.add(d.intValue());
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}
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}
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} catch (IOException e) {
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e.printStackTrace();
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} finally {
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try {
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reader.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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return labels;
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}
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private static List<String> readData(String positionFile) {
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List<String> positions = new ArrayList<>();
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FileInputStream fis = null;
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Scanner scanner = null;
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try {
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fis = new FileInputStream(positionFile);
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scanner = new Scanner(fis);
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while (scanner.hasNextLine()) {
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positions.add(scanner.nextLine());
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}
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} catch (FileNotFoundException e) {
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e.printStackTrace();
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} finally {
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try {
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scanner.close();
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fis.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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return null;
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}
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}
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@@ -0,0 +1,225 @@
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package edu.lu.uni.serval.FixPatternMining.App;
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import java.io.File;
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import java.io.FileInputStream;
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import java.io.FileNotFoundException;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.Scanner;
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import edu.lu.uni.serval.FixPatternMining.DataPrepare.DataPreparation;
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import edu.lu.uni.serval.config.Configuration;
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import edu.lu.uni.serval.utils.FileHelper;
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import edu.lu.uni.serval.utils.ListSorter;
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import edu.lu.uni.serval.utils.MapSorter;
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/**
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* Compute similarities for each potential bug instance by computing the similarities with all instances in related cluster.
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*
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* List top 5 most similar instances?
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*
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* @author kui.liu
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*
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*/
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public class Step14 {
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public static void main(String[] args) {
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String featuresOfTrainingDataPath = Configuration.FEATURES_OF_COMMON_CLUSTERS;
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List<File> featuresOfTrainingDataFiles = FileHelper.getAllFiles(featuresOfTrainingDataPath, ".csv");// TODO: type
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Map<Integer, List<String>> features = readFeaturesOfTrainingData(featuresOfTrainingDataFiles);
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Map<Integer, List<String>> patches = readPatchesOfTraingData(Configuration.CLUSTERED_PATCHES_FILE);
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Map<Integer, Integer> labelMapClusterNum = DataPreparation.readLabelMapClusterNum();
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// potential bugs' information
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String bugs90 = Configuration.TESTING_DATA_BUGS90;
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String bugs80 = Configuration.TESTING_DATA_BUGS80;
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String bugs70 = Configuration.TESTING_DATA_BUGS70;
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String bugs60 = Configuration.TESTING_DATA_BUGS60;
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List<String> bugsList = new ArrayList<>();
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bugsList.add(bugs90);
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bugsList.add(bugs80);
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bugsList.add(bugs70);
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bugsList.add(bugs60);
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for (String bugs : bugsList) {
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List<String> bugsInfo = readData(bugs);
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String filePath = bugs.substring(0, bugs.lastIndexOf(".")) + "/";
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StringBuilder builder = new StringBuilder();
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for (String singleBugInfo : bugsInfo) {
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String[] infoArray = singleBugInfo.split(":");
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String label = infoArray[0];// TODO
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String feature = infoArray[1]; // TODO
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String position = infoArray[2]; // TODO
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int labelInt = Integer.parseInt(label);
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int clusterNum = labelMapClusterNum.get(labelInt);
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Map<Double, Integer> mostSimilarIndex = computeSimilarities(feature, features.get(clusterNum));
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List<Integer> patchesIndex = new ArrayList<>();
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if (mostSimilarIndex.size() > 0) {
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for (Map.Entry<Double, Integer> entry : mostSimilarIndex.entrySet()) {
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patchesIndex.add(entry.getValue());
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}
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String bug = "BUG####" + position + "\n";
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String patchesStr = readPatches(patchesIndex, patches.get(Integer.parseInt(label)));
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// output: bug + patchesStr;
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builder.append(bug).append(patchesStr);
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}
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}
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FileHelper.outputToFile(filePath + "patches.list", builder, false);
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}
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}
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private static Map<Integer, List<String>> readPatchesOfTraingData(String clusteredPatchesFile) {
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Map<Integer, List<String>> map = new HashMap<>();
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List<File> files = FileHelper.getAllFiles(clusteredPatchesFile, ".list");
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for (File file : files) {
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String fileName = file.getName();
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int clusterNum = Integer.parseInt(fileName.substring(fileName.lastIndexOf("_") + 1, fileName.lastIndexOf(".")));
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List<String> patches = readPatches(file);
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map.put(clusterNum, patches);
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}
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return map;
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}
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private static List<String> readPatches(File file) {
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List<String> patches = new ArrayList<>();
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FileInputStream fis = null;
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Scanner scanner = null;
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try {
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fis = new FileInputStream(file);
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scanner = new Scanner(fis);
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String singlePatch = "";
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while (scanner.hasNextLine()) {
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String line = scanner.nextLine();
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if (Configuration.PATCH_SIGNAL.equals(line)) {
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if (!"".equals(singlePatch)) {
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patches.add(singlePatch);
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singlePatch = "";
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}
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}
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singlePatch += line + "\n";
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}
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patches.add(singlePatch);
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} catch (FileNotFoundException e) {
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e.printStackTrace();
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} finally {
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try {
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scanner.close();
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fis.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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return patches;
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}
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private static String readPatches(List<Integer> patchesIndex, List<String> patchesTrainingData) {
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String patches = "";
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for(Integer index : patchesIndex) {
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patches += patchesTrainingData.get(index) + "\n";
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}
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return patches;
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}
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private static Map<Double, Integer> computeSimilarities(String feature, List<String> trainingFeatures) {
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Map<Double, Integer> mostSimilarIndex = new HashMap<>();
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List<Double> similarities = new ArrayList<>();
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for (int i = 0; i < 10; i ++) similarities.add(0.0);
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for (int index = 0, size = trainingFeatures.size(); index < size; index ++) {
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String trainingFeature = trainingFeatures.get(index);
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double similarity = computeSimilarity(feature, trainingFeature);
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double aborted = addToSimilarityies(similarity, similarities);
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if (aborted > 0.0) {
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mostSimilarIndex.put(similarity, index);
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if (aborted == 0.1) mostSimilarIndex.remove(aborted);
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}
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}
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if (mostSimilarIndex.size() > 0) {
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MapSorter<Double, Integer> sorter = new MapSorter<>();
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mostSimilarIndex = sorter.sortByKeyDescending(mostSimilarIndex);
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}
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return mostSimilarIndex;
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}
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private static double addToSimilarityies(double similarity, List<Double> similarities) {
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double lastSimilarity = similarities.get(9);
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if (similarity >= 0.8 && similarity > lastSimilarity) { // TODO : 9 ?
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similarities.set(9, similarity);
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ListSorter<Double> sorter = new ListSorter<Double>(similarities);
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similarities = sorter.sortDescending();
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return lastSimilarity == 0.0 ? 0.1 : lastSimilarity;
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}
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return 0.0;
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}
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private static double computeSimilarity(String feature, String trainingFeature) {
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// TODO Auto-generated method stub
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return 0;
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}
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private static Map<Integer, List<String>> readFeaturesOfTrainingData(List<File> featureFiles) {
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Map<Integer, List<String>> features = new HashMap<>();
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for (File file : featureFiles) {
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String fileName = file.getName();
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String label = fileName.substring(fileName.lastIndexOf("_") + 1, fileName.lastIndexOf("."));
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int clusterNum = Integer.parseInt(label);
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List<String> featuresList = readData(file.getPath());
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features.put(clusterNum, featuresList);
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}
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return features;
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}
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private static List<String> readData(String positionFile) {
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List<String> positions = new ArrayList<>();
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FileInputStream fis = null;
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Scanner scanner = null;
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try {
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fis = new FileInputStream(positionFile);
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scanner = new Scanner(fis);
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while (scanner.hasNextLine()) {
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positions.add(scanner.nextLine());
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}
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} catch (FileNotFoundException e) {
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e.printStackTrace();
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} finally {
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try {
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scanner.close();
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fis.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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return null;
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}
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public static void quickSort(List<Double> arr){
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qsort(arr, 0, arr.size() - 1);
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}
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private static void qsort(List<Double> arr, int low, int high){
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if (low < high){
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int pivot=partition(arr, low, high); //将数组分为两部分
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qsort(arr, low, pivot-1); //递归排序左子数组
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qsort(arr, pivot+1, high); //递归排序右子数组
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}
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}
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private static int partition(List<Double> arr, int low, int high){
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double pivot = arr.get(low); //枢轴记录
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while (low<high){
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while (low<high && arr.get(high)>=pivot) --high;
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arr.set(low, arr.get(high)); //交换比枢轴小的记录到左端
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while (low<high && arr.get(low)<=pivot) ++low;
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arr.set(high, arr.get(low)); //交换比枢轴小的记录到右端
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}
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//扫描完成，枢轴到位
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arr.set(low, pivot);
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//返回的是枢轴的位置
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return low;
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}
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}
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@@ -557,4 +557,105 @@ public class DataPreparation {
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}
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return commonClustersMappingLabel;
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}
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public static void separateTrainingDataFeatures() {
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String trainingDataFeatures = Configuration.FEATURES_OF_TRAINING_DATA;
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List<File> featureFiles = FileHelper.getAllFilesInCurrentDiectory(trainingDataFeatures, ".csv");
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File featureFile = featureFiles.get(0);
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// File featureFile = new File(Configuration.FEATURES_OF_TRAINING_DATA + "");
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Map<Integer, Integer> numbersMap = readNumberOfInstances(); // <Integer, Integer>: <clusterNum, numOfInstances>
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Map<Integer, Integer> orders = new HashMap<>(); //<Integer, Integer> : <order, clusterNum>
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Map<Integer, String> fileNames = new HashMap<>();
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String clusteredTokens = Configuration.CLUSTERED_TOKENSS_FILE;
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List<File> files = FileHelper.getAllFilesInCurrentDiectory(clusteredTokens, ".list");
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int order = 1;
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for (File file : files) {
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String fileName = file.getName();
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String clusterNumStr = fileName.substring(fileName.lastIndexOf("_") + 1, fileName.lastIndexOf(".list"));
|
||||
int clusterNum = Integer.parseInt(clusterNumStr);
|
||||
if (numbersMap.containsKey(clusterNum)) {
|
||||
orders.put(order, clusterNum);
|
||||
fileNames.put(order, fileName);
|
||||
order ++;
|
||||
}
|
||||
}
|
||||
|
||||
String featuresOfClusterPath = Configuration.FEATURES_OF_COMMON_CLUSTERS;
|
||||
order = 1;
|
||||
FileInputStream fis = null;
|
||||
Scanner scanner = null;
|
||||
try {
|
||||
fis = new FileInputStream(featureFile);
|
||||
scanner = new Scanner(fis);
|
||||
int counter = 0;
|
||||
StringBuilder features = new StringBuilder();
|
||||
while (scanner.hasNextLine()) {
|
||||
features.append(scanner.nextLine() + "\n");
|
||||
counter ++;
|
||||
if (counter == numbersMap.get(orders.get(order))) {
|
||||
FileHelper.outputToFile(featuresOfClusterPath + fileNames.get(order), features, false);
|
||||
features.setLength(0);
|
||||
counter = 0;
|
||||
order ++;
|
||||
}
|
||||
}
|
||||
} catch (FileNotFoundException e) {
|
||||
e.printStackTrace();
|
||||
} finally {
|
||||
try {
|
||||
scanner.close();
|
||||
fis.close();
|
||||
} catch (IOException e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private static Map<Integer, Integer> readNumberOfInstances() {
|
||||
Map<Integer, Integer> numbersMap = new HashMap<>();
|
||||
String fileContent = FileHelper.readFile(Configuration.COMMON_CLUSTERS_SIZES);
|
||||
BufferedReader reader = null;
|
||||
try {
|
||||
reader = new BufferedReader(new StringReader(fileContent));
|
||||
String line = null;
|
||||
while ((line = reader.readLine()) != null) {
|
||||
String[] numbers = line.split(":");
|
||||
numbersMap.put(Integer.parseInt(numbers[0]), Integer.parseInt(numbers[1]));
|
||||
}
|
||||
} catch (IOException e) {
|
||||
e.printStackTrace();
|
||||
} finally {
|
||||
try {
|
||||
reader.close();
|
||||
} catch (IOException e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
return numbersMap;
|
||||
}
|
||||
|
||||
public static Map<Integer, Integer> readLabelMapClusterNum() {
|
||||
Map<Integer, Integer> labelMapClusterNumMap = new HashMap<>();
|
||||
String fileContent = FileHelper.readFile(Configuration.CLUSTERNUMBER_LABEL_MAP);
|
||||
BufferedReader reader = null;
|
||||
reader = new BufferedReader(new StringReader(fileContent));
|
||||
String line = null;
|
||||
try {
|
||||
while ((line = reader.readLine()) != null) {
|
||||
String[] labelMapClusterNum = line.split(":");
|
||||
labelMapClusterNumMap.put(Integer.parseInt(labelMapClusterNum[0]), Integer.parseInt(labelMapClusterNum[1]));
|
||||
}
|
||||
} catch (IOException e) {
|
||||
e.printStackTrace();
|
||||
}finally {
|
||||
try {
|
||||
reader.close();
|
||||
} catch (IOException e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user