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NLP2API: Query Reformulation for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics

Accepted Papers at ICSME 2018

**TCSE Distinguished Paper Award Nomination**

Effective Reformulation of Query for Code Search using  Crowdsourced Knowledge and Extra-Large Data Analytics 

Mohammad Masudur Rahman and Chanchal K. Roy

Download this paper: PDF

NLP2API: Query Reformulation for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics 

Mohammad Masudur Rahman and Chanchal K. Roy

Download this paper: PDF

Abstract: Software developers frequently issue generic natural language queries for code search while using code search engines (e.g., GitHub native search, Krugle). Such queries often do not lead to any relevant results due to vocabulary mismatch problems. In this paper, we propose a novel technique that automatically identifies relevant and specific API classes from Stack Overflow Q & A site for a programming task written as a natural language query, and then reformulates the query for improved code search. We first collect candidate API classes from Stack Overflow using pseudo-relevance feedback and two term weighting algorithms, and then rank the candidates using Borda count and semantic proximity between query keywords and the API classes. The semantic proximity has been determined by an analysis of 1.3 million questions and answers of Stack Overflow. Experiments using 310 code search queries report that our technique suggests relevant API classes with 48% precision and 58% recall which are 32% and 48% higher respectively than those of the state-of-the-art. Comparisons with two state- of-the-art studies and three popular search engines (e.g., Google, Stack Overflow, and GitHub native search) report that our reformulated queries (1) outperform the queries of the state- of-the-art, and (2) significantly improve the code search results provided by these contemporary search engines.

Distinguished Paper Award Nomination Artifact Verified by ICSME 2018

Do you want to check RACK also?

Artifact Download Links

  • You can download from Google drive
  • You can also clone the replication package from our GitHub Repository using the following command: 
    git clone https://github.com/masud-technope/NLP2API-Replication-Package.git NLP2API

cd NLP2API
  
sh INSTALL.sh
    Please send me ([email protected]) an email or create an issue report if the INSTALL script does not work

Getting Started

  • Execute jdk-fasttext-checker to check whether your system meets the tool's requirements.
  • NLP2API might work sub-optimally or might not work at all if the system requirements are not properly met.

Materials Included (23)

  • INSTALL.sh : The script downloads and unzips large files from Google Drive.

  • nlp2api-runner.jar : The working prototype of NLP2API (cross-platform). Version 0.0.0 is windows-based only.

  • NLP2API: SOURCE CODE can be cloned from this repository

  • data : It contains stop words and Java programming keywords

  • candidate : Auxiliary folder storing candidate API classes

  • dataset/qa-corpus-ext-index: Lucene index of programming Q & A threads of Stack Overflow

  • dataset/answer-ext.7z : It contains the HTML source of Stack Overflow answers

  • dataset/question-ext.7z : It contains the HTML source of Stack Overflow questions (You need to unzip these files)

  • dataset/answer-norm-code-ext-index : Lucene index of answer code segments of Stack Overflow

  • dataset/question-norm-code-ext-index : Lucene index of question code segments of Stack Overflow

  • scripts : It contains batch script to access fastText model

  • fastText.7z : It contains our trained skip-gram model and fastText implementation using gensim. It is cross-platform hopefully. (You need to unzip this file manually if INSTALL script fails. Once decompressed, you need to execute FastTextChecker.py to make sure that fastText is working

  • fastText-windows.7z : It contains our trained skip-gram model and fastText tool (windows-based). (You need to unzip this file and make sure the fasttext command is working on your platform. More details on this tool's dependencis can be found here Our model was developed using Windows version of this fastText)

    You can choose one of these two versions based on your platform

  • NL-Query+GroundTruth : It contains NL query and ground truth API classes (i.e., the order is important)

  • NLP2API-Results-Borda : It contains NL query and suggested API classes (Borda)

  • NLP2API-Results-Q-A-Proximity : It contains NL query and suggestd API classes (Q-A proximity)

  • NLP2API-Results : It contains NL query and suggestd API classes of NLP2API (i.e., both proxies combined)

  • oracle-310 : NL queries and ground truth API classes

  • code-ext-index : Lucene index of code segment corpus (310 ground truth code segments + 3,860 other code segments)

  • lib : It contains all the dependency files (Optional). The tool is an executable JAR file, and hence already packages all the dependencies except those required by fastText.

  • jdk-fasttext-checker : It checks for Java 8 and fastText installations and their operation integrity.

  • FastTextChecker.py : It checks the installation of fastText

  • README

  • LICENSE

System Requirements

  • JDK: NLP2API was built with JDK 1.8.0_74. Please use JDK 1.8.* for the successful execution/run. JDK 10 fails to load several legacy dependencies of NLP2API.
  • Operating System: Cross-plaform (nlp2api-runner.jar), Windows 10 (nlp2api-runner-0.0.0.jar)
  • The path to the directory containing NLP2API materials should not contain any space characters.
  • Every compressed file should be de-compressed in the same directory. For example, dataset/answer-ext.7z should be dataset/answer-ext.
  • Make sure that fastText is working on your platform. Run FastTextChecker.py for checking. For windows-based fastText, go to /fastText directory and execute fasttext on the Windows command line. If it shows the available options, then fastText is working. Otherwise, you have to take care of its dependencies.

Available Operations

  • reformulate : Returns a list of API classes for one or more NL queries.
  • evaluate-as : Evaluates the accuracy of suggested API classes against ground truth.
  • evaluate-qe : Evaluates improvement, worsening and preserving of baseline queries by NLP2API.
  • evaluate-cs : Evaluates the code retrieval performance of queries.
  • evaluate-se : Evaluates the improvement of code search results by search engines when reformulated queries (of NLP2API) are used.

Required parameters for the operations

  • -K : expects the number of suggested API classes (e.g., default: 10)
  • -query : expects a natural language query
  • -queryFile : expects the file containing the natural language query (e.g., deafult: ./NL-Query+GroundTruth.txt)
  • -outputFile : expects the output file name (e.g., default: ./NLP2API-queries.txt)
  • -resultFile : same as outputFile
  • -se : expects the name of a search engine (e.g., google, stackoverflow, github)

Q.1: How to install the NLP2API tool?

  • Download all items from the Google drive or GitHub, and keep in /home folder.
  • Unzip all zip files, and make sure that they are in the home directory. For example, dataset/question-ext.7z should be /home/dataset/question-ext
  • check Java and fastText installations using jdk-fasttext-checker.
  • Run the tool from within the home directory.

Q.2: How to reformulate a given NL query or a file containing all the queries?

Reformulate a single query

java -jar nlp2api-runner.jar -K 10 -task reformulate -query How do I send an HTML email?

Reformulate all queries stored in a file

java -jar nlp2api-runner.jar -K 10 -task reformulate -inputFile ./NL-Query+GroundTruth.txt -outputFile apiresults.txt

Please note that each NL query is followed by ground truth API classes in the next line. If you want to create a custom query file, please keep the queries at the odd lines. The reformulation of 310 queries takes a few minutes. The output file will be created inside the "home/result/" folder.

Query File format

  • NL Query: How do I send an HTML email?
  • Ground Truth: Properties Session Message MimeMessage InternetAddress

Q.3: How to determine API suggestion performance?

java -jar nlp2api-runner.jar -K 10 -task evaluate-as -resultFile ./NLP2API-Results.txt

This command reports Top-10 accuracy, MRR@10, MAP@10, and MR@10 for API suggestion

Q.4: How to determine query improvement and worsening ratios of the reformulated queries?

java -jar nlp2api-runner.jar -K 5 -task evaluate-qe -resultFile ./NLP2API-Results.txt

This commands reports query improvement, worsening, preserved ratios and mean rank differences with the initial queries.

Q.5: How to get retrieval performance of the reformulated queries?

java -jar nlp2api-runner.jar -K 10 -task evaluate-cs -resultFile ./NLP2API-Results.txt

This commands reports Top-10 accuracy and MRR@10 of code segment retrieval by NLP2API

Q.6: How much improvements can be made over traditional web/code search engines' performance?

java -jar nlp2api-runner.jar -K 10 -task evaluate-se -se google

This command reports Google's Top-10 performance with NL queries, and subsequent improvements using our reformulated queries. Possible se values are: google, stackoverflow and github

Please cite our work as

@INPROCEEDINGS{icsme2018masud,
author={Rahman, M. M. and Roy, C. K.}, 
booktitle={Proc. ICSME}, 
title={Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics}, 
year={2018}, 
pages={12} 
}

Download this paper: PDF

@INPROCEEDINGS{icsme2018masudb, 
author={Rahman, M. M. and Roy, C. K.}, 
booktitle={Proc. ICSME}, 
title={NLP2API: Query Reformulation for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics }, 
year={2018}, 
pages={1} 
}

Download this paper: PDF

Something not working as expected?

Contact: Masud Rahman ([email protected])

OR

Create an issue from here