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ABOUT

PERSONAL DETAILS
1017 Academic Way, Tallahassee, FL 32304
escobara@cs.fsu.edu
Hello. I am a Sonia’s Student Researcher Teaching Assistant Programmer Colombian Metalhead
I am passionate about programming and coding and life

BIO

ABOUT ME

Hi. I am a PhD Student in Computer Science at Florida State University advised by Dr. Sonia Haiduc. My area of research is Software Engineering, particularly focused on:

  • Software Maintenance and Evolution,
  • Information Retrieval,
  • Multimedia data sources,
  • Mining Software Repositories,
  • Machine learning, and
  • Natural Language Processing.
I am part of the SERENE research group at FSU.

HOBBIES

INTERESTS

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FACTS

NUMBERS ABOUT ME

920
CUPS OF COFFEE
65
PROJECTS COMPLETED
2965
HOURS OF CODING
35
WORKSHOPS
2M
LINES OF CODE
100
SATISFIED CUSTOMERS

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RESUME (IN PDF)

  • EDUCATION
  • 2012
    2015
    Bogotá,COLOMBIA

    SYSTEMS ENGINEERING AND COMPUTER SCIENCE - MS

    UNIVERSIDAD NACIONAL DE COLOMBIA

     

  • 2006
    2010
    Bogotá,COLOMBIA

    SYSTEMS ENGINEERING - BS

    UNIVERSIDAD NACIONAL DE COLOMBIA

     

  • ACADEMIC AND PROFESSIONAL POSITIONS
  • 2014
    2017
    Tallahassee

    GRADUATE RESEARCHER ASSISTANT

    FLORIDA STATE UNIVERSITY

     

  • 2016
    2017
    Tallahassee

    GRADUATE TEACHING ASSISTANT

    FLORIDA STATE UNIVERSITY

     

  • 2009
    2014
    Bogotá,COLOMBIA

    SOFTWARE ENGINEER

  • HONORS AND AWARDS
  • 2015
    Florence, ITALY

    ACM Student Research Competition at ICSE’15

    SECOND PLACE

    Automatic Categorization of Software Libraries Using Bytecode. Awarded by ACM SIGSOFT.
  • 2015

    ACM SIGSOFT CAPS travel award

    FOR ICSE 2015

    Awarded by ACM SIGSOFT.
  • 2015
    Bogotá, COLOMBIA

    MASTER THESIS DISTINCTION

    A Model for Automatic Categorization of Software Applications using Non-Parametric Clustering and Bytecode Analysis. Awarded by Universidad Nacional de Colombia.
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PUBLICATIONS

PUBLICATIONS LIST
24 MAY 2017

TEXT RETRIEVAL-BASED TAGGING OF SOFTWARE ENGINEERING VIDEO TUTORIALS

ICSE 2017 - BUENOS AIRES - ARGENTINA

We present the first efforts towards new tagging approaches using text retrieval that describe the contents of software engineering video tutorials.

Conferences Javier Escobar-Avila, Esteban Parra, Sonia Haiduc
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Text retrieval-based tagging of software engineering video tutorials

Javier Escobar-Avila, Esteban Parra, Sonia Haiduc Conferences

Video tutorials are an emerging form of documentation in software engineering and can efficiently provide developers with useful information needed for their daily tasks. However, to get the information they need, developers have to find the right tutorial for their task at hand. Currently, there is little information available to quickly judge whether a tutorial is relevant to a topic or helpful to the task at hand, which can lead to missing the best tutorials and wasting time watching irrelevant ones. We present the first efforts towards new tagging approaches using text retrieval that describe the contents of software engineering video tutorials, making it easier and faster to understand their purpose and contents. We also present the results of a preliminary evaluation of thirteen such approaches, revealing the potential of some and limitations of others.

16 MAY 2015

UNSUPERVISED SOFTWARE CATEGORIZATION USING BYTECODE

ICPC 2015 - FLORENCE - ITALY

We propose a novel approach that uses semantic information recovered from byte code and an unsupervised algorithm to assign categories to software systems.

Conferences Javier Escobar-Avila, Mario Linares-Vásquez, Sonia Haiduc
img

Unsupervised Software Categorization Using Bytecode

Javier Escobar-Avila, Mario Linares-Vásquez, Sonia Haiduc Conferences

Automatic software categorization is the task of assigning software systems or libraries to categories based on their functionality. Correctly assigning these categories is essential to ensure that relevant software can be easily retrieved by developers from large repositories. State of the art approaches either rely on the availability of the source code, or use supervised machine learning approaches, which require a set of already labeled software as training data. These restrictions make current approaches fail when such information is not available. We propose a novel approach, which overcomes these limitations by using semantic information recovered from byte code and an unsupervised algorithm to assign categories to software systems. We evaluated our approach in a study on the Apache Foundation Repository of Java libraries and the results indicate that our approach is able to correctly identify a correct category for 86% of the libraries.

16 MAY 2015

AUTOMATIC CATEGORIZATION OF SOFTWARE LIBRARIES USING BYTECODE

ICSE 2015 - FLORENCE - ITALY

We propose a novel unsupervised approach for the automatic categorization of Java libraries, which uses the bytecode of a library in order to determine its category.

Conferences Javier Escobar-Avila, Mario Linares-Vásquez, Sonia Haiduc
img

Unsupervised Software Categorization Using Bytecode

Javier Escobar-Avila, Mario Linares-Vásquez, Sonia Haiduc Conferences

Automatic software categorization is the task of assigning categories or tags to software libraries in order to summarize their functionality. Correctly assigning these categories is essential to ensure that relevant libraries can be easily retrieved by developers from large repositories. Current categorization approaches rely on the semantics reflected in the source code, or use supervised machine learning techniques, which require a set of labeled software as a training data. These approaches fail when such information is not available. We propose a novel unsupervised approach for the automatic categorization of Java libraries, which uses the bytecode of a library in order to determine its category. We show that the approach is able to successfully categorize libraries from the Apache Foundation Repository.

15 JUL 2015

A MODEL FOR AUTOMATIC CATEGORIZATION OF SOFTWARE APPLICATIONS USING NON-PARAMETRIC CLUSTERING AND BYTECODE ANALYSIS

BOGOTÁ - COLOMBIA

In this document, we describe a novel approach for the automatic categorization of Java libraries, which needs only the bytecode of a library in order to determine its category. We show that the approach, based on Dirichlet Process Clustering with automatic labeling, is able to successfully categorize libraries from the Apache Foundation Repository.

Theses Javier Escobar-Avila
img

A model for automatic categorization of software applications using non-parametric clustering and bytecode analysis

Javier Escobar-Avila Theses

Automatic software categorization is the task of assigning software systems or libraries to categories based on their functionality. Correctly assigning these categories is essential to ensure that relevant libraries can be easily retrieved by developers from large repositories. State of the art approaches rely on the semantics reflected by identifiers and comments in the source code of the libraries in order to determine their category. However, these approaches fail when the source code of the libraries is not available. In this document, we describe a novel approach for the automatic categorization of Java libraries, which needs only the bytecode of a library in order to determine its category. We show that the approach, based on Dirichlet Process Clustering with automatic labeling, is able to successfully categorize libraries from the Apache Foundation Repository.

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RESEARCH

LABORATORY TEAM

SONIA HAIDUC

ASSISTANT PROFESSOR

Research topics: software maintenance and evolution, program comprehension, concept and bug location, mining software repositories, empirical studies in software engineering.

Go to her web page >

CHRIS MILLS

PHD STUDENT

Research topics: large-scale code search, concept and bug location, applications of machine learning, information retrieval and natural language processing in software engineering, mining software repositories.

ESTEBAN PARRA

PHD STUDENT

Research topics: software summarization, concept and bug location, empirical software engineering, software visualization.

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TEACHING

  • CURRENT
  • SPRING 2017

    TEACHING ASSISTANT

    FLORIDA STATE UNIVERSITY

    CEN 4021 – Software Engineering II
  • TEACHING HISTORY
  • FALL 2016

    TEACHING ASSISTANT

    FLORIDA STATE UNIVERSITY

    CEN 5035 – Graduate Software Engineering
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CONTACT

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