Source code and data set of my thesis submitted to Nanjing University of Posts and Telecommunications for the degree of master of engineering
南京邮电大学工程硕士毕业论文《基于学习排序的软件缺陷定位技术研究与实现》数据集及源代码。
RSLocatorSystem文件夹为系统及UI实现，不必深究，实验步骤代码参考trial文件夹。
The file folder "RSLocatorSystem" is the prototype system showed in my thesis. It's a MyEclipse project. You can import it into your Eclipse and edit it. To run it, you should implement NLTK(http://www.nltk.org/) first.
The file folder "trial" contains data set and source code of trials in my thesis.
Query set and Corpus of Apache Spark are in the "SparkDataSet". Bug reports of 1.6.0 and 1.6.1 are in the file "bugReport.txt", and source code of 1.5.2 is in the file "sourceCode.txt". The corpus of file paths is the file "path.txt". The three corpus mentioned above are in the same order. The file "codeQuery.txt", "codeCorpus.txt", "componentQuery.txt", "componentCorpus.txt", "identifierQuery.txt", "identifierCorpus.txt" are preprocessed queries and corpus, which are able to be used to conduct experiments directly.
SparkDataSet文件夹为数据集：sourceCode.txt为整个项目源代码，每行为一个代码文件；bugReport.txt为bug报告，每行为一个报告，path.txt为每个bug报告实际对应的代码文件，report-file.csv文件为每个bug对应报告在path.txt中的行数。xxxxQuery和xxxxCorpus分别为bug报告和代码文件经过预处理后的结果。
The preprocess scipts are in the "preprocess" file folder, which is writeen by Python. To run these scripts, you need to implement NLTK. The "reportPreprocess.py" is used to preprocess bug reports, and "srcPreprocess.py" is used to preprocess source code file. In this trial, all the reports are in one file, in which one report is in a line. So does the source code.
preprocess文件夹中为文本预处理脚本，reportPreprocess.py和srcPreprocess.py分别为报告和代码预处理脚本
"BM25" and "VSM" are the information retrieve models. Befor running it, you need to create two files named "query.txt" and "corpus.txt". Thankss to uyaseen(https://github.com/uyaseen/vsm) and nhirakawa(https://github.com/nhirakawa/BM25).
BM25和VSM文件夹为两个经典检索算法实现
The Ranking SVM tool is in the file folder "ranking SVM". Its author is Thorsten Joachims(http://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html). Before ranking, test set should be created. So you should run "DataSetProducer.java" in the Evaluation/src/ranking SVM. In my trial, I used the linux version. To create test sets in correct format, you should run a shell script similar to "rank.sh". In the prototype system, windows version is used.
ranking SVM文件夹为该算法代码，功能为将三个维度（code、component、identifier）的相关度综合排序
To evalute the performance of trial, you need a small programme, and it's in the "Evalution". It's writeen in Java.To run it, you need to change some variables, such as file path, in the "PublicValue.java".
Evaluation文件夹为系统的优化改进工作