Toward Mining Concept Keywords from Identifiers in Large Software - PowerPoint PPT Presentation

Jan 18, 2023 •112 likes •160 views

Toward Mining Concept Keywords from Identifiers in Large Software Projects Masaru Ohba and Katsuhiko Gondow Tokyo Institute of Technology What are concept keywords? Most programmers try to name identifiers meaningfully.

Toward Mining “Concept Keywords” from Identifiers in Large Software Projects Masaru Ohba and Katsuhiko Gondow Tokyo Institute of Technology
What are “concept keywords”? • Most programmers try to name identifiers meaningfully. • Concept keywords are defined terms that describe key concepts to aid in as program understanding. – e.g. read_dirent() : dirent is a concept keyword. dirent, root, PTE, tss, Concept keywords path, signal, yield Grouping words kbd , vga , FAT12 , sys , H, t Attributes, busy, byte, offset, name, less important concepts memory, end, int8, again read, set, is, move, wait, Generic verbs print, dump, make, init Human-selected concept keywords and other category words in udos
Suggestion • We should use more “concept keywords” in program understanding tools ． – concept keywords are concise and descriptive • Our solution: – provides a way to mine concept keywords. • ckTF/IDF methods / Identifier Exploratory Framework – could be used to build tools that support and utilize extracted concept keywords (future work).
Future work • Applying concept keywords to a Bug Tracking System (BTS) to see the relationship between bug report and corresponding problem source code. fat12.c read_ dirent () { Bug-report no.1 return NULL; Overview: dirent } It could not read directories. task.c signal sys_ signal (){ sys_kill(); Bug-report no.3 } Overview: I could not catch system calls. Concept keyword can bridge the gap between bug-reports and source code.

Recommend

Mining Large Single Networks under Subgraph Mining Large Single Networks under Subgraph

Mining Large Single Networks under Subgraph Mining Large Single Networks under Subgraph Homomorphism Homomorphism Mostafa H. Chehreghani Jan Ramon Thomas Fannes 12/13/13 Overview Introduction Problem definition and preliminaries

547 views • 13 slides

SML CONCEPT 1 SML CONCEPT 1 SML CONCEPT 1 Design 1 Main features 1 Large yoke arms and yoke

SML CONCEPT 1 SML CONCEPT 1 SML CONCEPT 1 Design 1 Main features 1 Large yoke arms and yoke base 2 Deep cast aluminium pan tube base 3 Pan tube base can incorporate components as well as display and keypad 4 Separate double ended handle

509 views • 38 slides

Using DNS for Mapping Using DNS for Mapping Host Identifiers to Locators Host Identifiers to

Using DNS for Mapping Using DNS for Mapping Host Identifiers to Locators Host Identifiers to Locators Oleg Ponomarev 24 March 2009 IETF74, San Francisco OUTLINE OUTLINE Current situation Storage conventions Usage HOST IDENTITY

225 views • 18 slides

Mining Large Datasets: Case of Mining Graph Data in the Cloud Sabeur Aridhi PhD in Computer

Background Contributions Conclusion Mining Large Datasets: Case of Mining Graph Data in the Cloud Sabeur Aridhi PhD in Computer Science with Laurent dOrazio, Mondher Maddouri and Engelbert Mephu Nguifo 16/05/2014 Sabeur Aridhi Mining

1.36k views • 68 slides

Contents Text Mining Concept Tasks Twitter Data Analysis with R Twitter Extracting Tweets

Text Mining with R Yanchang Zhao http://www.RDataMining.com R and Data Mining Course Beijing University of Posts and Telecommunications, Beijing, China July 2019 Chapter 10: Text Mining, in R and Data Mining: Examples and Case Studies

904 views • 64 slides

Verilog HDL:Digital Design and Modeling Chapter 3 Keywords Chapter 3 Keywords 2

Chapter 3 Keywords 1 Verilog HDL:Digital Design and Modeling Chapter 3 Keywords Chapter 3 Keywords 2 Page 80 //4:1 multiplexer using a always @ (sel or data) //case statement begin module mux_4_1_case (sel, case (sel) data,

270 views • 10 slides

DATA MINING LECTURE 1 Introduction What is data mining? After years of data mining there is

DATA MINING LECTURE 1 Introduction What is data mining? After years of data mining there is still no unique answer to this question. A tentative definition: Data mining is the use of efficient techniques for the analysis of very large

1.28k views • 62 slides

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data mining is the use of efficient techniques for the analysis of very large collections of data and the extraction of useful and possibly unexpected

2.4k views • 94 slides

Web Mining Web Mining Web Mining Web Mining Web mining is the use of data mining techniques

What is Web Mining? Wh t i W b Mi i What is Web Mining? Wh t i W b Mi i ? ? Web Mining Web Mining Web Mining Web Mining Web mining is the use of data mining techniques to automat cally d scover and extract nformat on automatically

774 views • 20 slides

Keywords can become your best marketing tool ever! How keyword use evolved and how it can be to

Keywords can become your best marketing tool ever! How keyword use evolved and how it can be to your benefit Presentation by Marcom April 16th 2020 Introduction Overview All about keywords and SEO Mark Baines (Chartered

243 views • 13 slides

Topic: SQAK: Doing More with Keywords Speaker: YINGJING YAN Why SQAK? Todays

Topic: SQAK: Doing More with Keywords Speaker: YINGJING YAN Why SQAK? Todays enterprise databases are large and complex, o=en rela>ng hundreds of en>>es.

1.38k views • 88 slides

Bay Area UASI Large Special Event Concept of Operations Template Project Janell M Myhre re

Bay Area UASI Large Special Event Concept of Operations Template Project Janell M Myhre re Regional al P Program am Manag ager April 14, 2016 Purpose Develop Emergency Management Large Special Event (LSE) Concept of Operations

192 views • 7 slides

Large-scale Graph Mining @ Google NY Vahab Mirrokni Google Research New York, NY DIMACS

Large-scale Graph Mining @ Google NY Vahab Mirrokni Google Research New York, NY DIMACS Workshop Large-scale graph mining Many applications Friend suggestions Recommendation systems Security Advertising Benefits Big data available Rich

822 views • 71 slides

Contents Text Mining Concept Tasks Twitter Data Analysis with R Twitter Extracting Tweets

Text Mining with R Yanchang Zhao http://www.RDataMining.com Tutorial on Machine Learning with R The Melbourne Data Science Week 2017 1 June 2017 Chapter 10: Text Mining, in R and Data Mining: Examples and Case Studies .

733 views • 57 slides

Web Mining Web Mining Web mining is the use of data mining techniques to automatically

What is Web Mining? What is Web Mining? Web Mining Web Mining Web mining is the use of data mining techniques to automatically discover and extract information from Web documents/services (Etzioni, 1996, CACM 39(11)) Web mining aims to

570 views • 22 slides

Mining Large Dynamic Graphs and Tensors Kijung Shin Ph.D. Candidate (kijungs@cs.cmu.edu)

Mining Large Dynamic Graphs and Tensors Kijung Shin Ph.D. Candidate (kijungs@cs.cmu.edu) Thesis Committee Prof. Christos Faloutsos (Chair) Prof. Tom M. Mitchell Prof. Leman Akoglu Prof. Philip S. Yu Mining Large Dynamic Graphs

1.63k views • 127 slides

The Internet and Identifiers Paul V. Mockapetris Sigcomm 2005 8/23/2005 What are Todays

The Internet and Identifiers Paul V. Mockapetris Sigcomm 2005 8/23/2005 What are Todays Digital Identifiers? Conventions associating one piece of data to another www.nominum.com to see web page Anna Kournikova into

718 views • 49 slides

Normalizing Resource Identifiers using Lexicons in the Global Change Information System Linking

Normalizing Resource Identifiers using Lexicons in the Global Change Information System Linking Earth Science Identifiers, Concepts, and Communities Brian Duggan 13 , Curt Tilmes 2 , Steven Aulenbach 13 , Robert E. Wolfe 12 , Justin C. Goldstein

480 views • 28 slides

Mining Large Dynamic Graphs and Tensors Kijung Shin Ph.D. Student (kijungs@cs.cmu.edu) Thesis

Mining Large Dynamic Graphs and Tensors Kijung Shin Ph.D. Student (kijungs@cs.cmu.edu) Thesis Committee Prof. Christos Faloutsos (Chair) Prof. Tom M. Mitchell Prof. Leman Akoglu Prof. Philip S. Yu Mining Large Dynamic Graphs

1.71k views • 106 slides

Concept Drift Albert Bifet March 2012 COMP423A/COMP523A Data Stream Mining Outline 1.

Concept Drift Albert Bifet March 2012 COMP423A/COMP523A Data Stream Mining Outline 1. Introduction 2. Stream Algorithmics 3. Concept drift 4. Evaluation 5. Classification 6. Ensemble Methods 7. Regression 8. Clustering 9. Frequent

633 views • 26 slides

Support Vector Machines for Large Scale Text Mining in R Ingo Feinerer 1 Alexandros Karatzoglou 2

Support Vector Machines for Large Scale Text Mining in R Ingo Feinerer 1 Alexandros Karatzoglou 2 1 Vienna University of Technology, Austria 2 Telefonica Research, Spain COMPSTAT2010 Motivation Machine learning and data mining require

451 views • 13 slides

What is Data Mining Many Definitions Search for valuable information in large amounts of

What is Data Mining Many Definitions Search for valuable information in large amounts of data Automated or Semi Automated Exploration and Analysis of large volumes of data in order to discover meaningful patterns A step in KDD process

1.21k views • 54 slides

Data Mining Learning from Large Data Sets Lecture 8

Data Mining Learning from Large Data Sets Lecture 8 Clustering large data sets 263-5200-00L Andreas Krause Announcements Homework 4 out

1.09k views • 65 slides

Anonymizing your hacktop A brief tour of unique identifiers accessible by software @ Unique

Anonymizing your hacktop A brief tour of unique identifiers accessible by software @ Unique Identifiers Who cares? What are we talking about? Where are they? Laptops & Desktops Peripherals Smartphones Why

358 views • 19 slides