CV

Basics

Name Junayed Mahmud
Label PhD Candidate
Email junayed.mahmud@ucf.edu
Url https://jmahmud47.github.io

Work

  • 2023.08 - Present
    Graduate Research Assistant
    University of Central Florida
    Published 4 research papers
    • Utilized Large language models (LLMs) for graphical user interface (GUI)-based program repair
    • Assessed bug reproduction steps by mapping to the GUI elements utilizing LLMs and program analysis to provide feedback to bug reporters so that they can rewrite the steps if necessary
    • Utilized LLMs for automatically generating assertions to validate the existence of diverse types of reported failures (i.e., crash and non-crash) in Android applications to aid in regression testing
    • Addressed the limitations of code-to-comment-translation models and generated improved software documentation using transformer-based models and contrastive learning
  • 2021.05 - 2023.08
    Graduate Research Assistant
    George Mason University
    Published 6 research papers
    • Improved text-retrieval-based bug localization by leveraging GUI interaction data to mitigate the semantic gap between information in bug reports and code
    • Developed a program analysis tool that converts user-performed app actions into replayable scenarios and extracts detailed GUI information for automated testing and debugging
    • Built a chatbot for bug reporting to improve report quality and studied the usability of the tool
    • Analyzed the characteristics of diverse types of reproducible bug reports to build effective automated techniques for different bug report management activities
    • Generated automated software documentation using visual software data encoded in GUIs by fine-tuning neural image captioning models
    • Characterized the shortcomings of code-to-comment-translation models without relying on existing reference-based metrics in order to address the shortcomings in developing new models
  • 2019.08 - 2021.05
    Graduate Teaching Assistant
    George Mason University
    Assisted in the following courses:
    • CS367 (Computer Systems and Programming)
    • CS222 (Computer Programming for Engineers)
  • 2017.01 - 2019.03
    Software Engineer
    Samsung R&D Institute Bangladesh Ltd.
    • Worked in an iOS application named SmartThings, designed to enable users to monitor and control smart electronic devices or appliances through their phones
    • Worked on developing the IoTivity architecture, which enables seamless communication between cloud services and consumer electronics devices
    • Developed multiple GUIs for the SmartThings project

Education

Awards

Publications

Skills

Programming Languages
Python
Java
C
C++
Swift
Objective C
Perl
Kotlin
JavaScript
R
MATLAB
PHP
HTML
Machine Learning
Pytorch
Tensorflow
Mobile Development
Android
iOS

Languages

Bengali
Native speaker
English
Fluent

Interests

Reserach Topics
Software Engineering
Bug Reporting
Bug Localization
Program Repair
Automated Mobile Testing
Natural Language Processing for Software Engineering
Source Code Analysis

References

Assistant Professor Kevin Moran
University of Central Florida
Assistant Professor Oscar Chaparro
College of William and Mary
Professor Andrian Marcus
George Mason University

Projects

  • 2023.01 - 2024.06
    Utilizing Graphical User Interfaces (GUIs) for Bug Localization
    One of the most important tasks related to managing bug reports is localizing the fault so that a fix can be applied. As such, prior work has aimed to automate this task of bug localization by formulating it as an information retrieval problem, where potentially buggy files are retrieved and ranked according to their textual similarity with a given bug report. However, there is often a notable semantic gap between the information contained in bug reports and identifiers or natural language contained within source code files. For user-facing software, there is currently a key source of information that could aid in bug localization, but has not been thoroughly investigated - information from the GUI. We investigate the hypothesis that, for end user-facing applications, connecting information in a bug report with information from the GUI, and using this to aid in retrieving potentially buggy files, can improve upon existing techniques for bug localization. To examine this phenomenon, we conduct a comprehensive empirical study that augments four baseline techniques for bug localization with GUI interaction information from a reproduction scenario to (i) filter out potentially irrelevant files, (ii) boost potentially relevant files, and (iii) reformulate text-retrieval queries. To carry out our study, we source the current largest dataset of fully-localized and reproducible real bugs for Android apps, with corresponding bug reports, consisting of 80 bug reports from 39 popular open-source apps. Our results illustrate that augmenting traditional techniques with GUI information leads to a marked increase in effectiveness across multiple metrics, including a relative increase in Hits@10 of 13-18%. Additionally, through further analysis, we find that our studied augmentations largely complement existing techniques.