Replication Package of "Exploiting Vision-Language Models in GUI Reuse", a paper published in the 22nd International Conference on Systems and Software Reuse (ICSR), Ottawa, Canada, April 27 2025.
The authors are: Victoria Niu, Walaa Alshammari, Naga Mamata Iluru, Padmaja Vaishnavi Teeleti, Nan Niu, Tanmay Bhowmik, and Jianzhang Zhang.
Artifacts of the paper entitled:
A Study of Natural-Language and Vision-Language GUI Retrieval
Authors: Walaa Alshammari, Yitong Yang, Yinglin Wang, Nan Niu, Tanmay Bhowmik, Padmaja Vaishnavi Teeleti, and Naga Mamata Iluru
The content is:
A-relevance-judging-results.xlsx has five sheets recording the four judges' assessment and their inter-rater agreement levels;
B-GUI-retrieval-answer-set.xlsx specifies the relevance relations between 40 GUI images and 27 NL queries;
C-retrieval-results.xlsx contains top-10 NL-based results in one sheet, and top-5 NL-based and VL-based results in the other four sheets; and
D-human-subject-study-material.pdf documents the five GUI reuse tasks approved by an institutional review board.
D-
This is a poster detailing the scope, design, process safety, and economics for a chemical engineering capstone by project group 5046-2403. The project is centered around capturing carbon dioxide emissions from indoor testing cells at the General Electric Aerospace site in Peebles, Ohio. The process captures carbon dioxide from jet engine exhaust through a series of adsorption towers with activated carbon sorbent. The adsorbate goes through a desorption cycle to release purified gaseous carbon dioxide from the surface of the activated carbon. The gas is compressed for storage and off-site transport.
Artifacts of the paper entitled:
Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning
Authors: Hemanth Gudaparthi, Nan Niu, Boyang Wang, Tanmay Bhowmik, Hui Liu, Jianzhang Zhang, Juha Savolainen, Glen Horton, Sean Crowe, Thomas Scherz and Lisa Haitz
To appear in the Proceedings of the 31st IEEE International Requirements Engineering Conference (RE 2023 https://conf.researchr.org/home/RE-2023)
Replication Package of Environmental Variations of Software Features: A Logical Test Cases' Perspective authored by Md Rayhan Amin,Tanmay Bhowmik, Nan Niu, and Juha Savolainen
The Dataset contains raw data that indicates the start and stop time of water flowing at fixtures in the Marian Spencer Hall Cafeteria restroom during hours of operation. The data were collected as part of an effort to develop and test a novel method of measuring flow to calculate the probability that the fixture is busy (fixture p-value). The fixture p-value is one of the parameters necessary to predict peak demand in buildings for pipe sizing purposes.
There are two .csv files, a README file and a sample of the data collection template with contact information. The dataset also contains a MATLAB code written to accept data in the suggested format and estimate the fixture probability of use.
Aurek Chattopadhyay, Reagan Maddox, Glen Horton, Nan Niu, Ganesh Malla, Tanmay Bhowmik, Jianzhang Zhang, and Juha Savolainen, Completeness of Natural Language Requirements: A Comparative Study of User Stories and Feature Descriptions (submitted to REFSQ 2023: https://2023.refsq.org)
Until recently, nationally representative survey data has been the primary source of information on the energy performance of buildings in the U.S., relative to their year of construction. The emergence of municipal energy benchmarking ordinances and public availability of benchmarking datasets now makes it possible to explore these relationships at the local level, and to link this data with information about a building’s historic designation status. This paper presents results from an initial statistical analysis examining the relationships between building energy use, year of construction, and historic designation status. First, municipal benchmarking data from six U.S. cities is used to examine local trends in the relationship between building age and energy performance. Second, an exploratory analysis of the energy performance of designated historic compared to non-historic buildings in New York City is presented. The methods described in this paper could be applied more widely to benchmarking datasets from other cities.
