Software Engineering Commons^™

Automated Question Title Reformulation By Mining Modifcation Logs From Stack Overflow, Ke Liu, Xiang Chen, Chunyang Chen, Xiaofei Xie, Zhanqi Cui

Research Collection School Of Computing and Information Systems

In Stack Overflow, developers may not clarify and summarize the critical problems in the question titles due to a lack of domain knowledge or poor writing skills. Previous studies mainly focused on automatically generating the question titles by analyzing the posts’ problem descriptions and code snippets. In this study, we aim to improve title quality from the perspective of question title reformulation and propose a novel approach QETRA motivated by the findings of our formative study. Specifically, by mining modification logs from Stack Overflow, we first extract title reformulation pairs containing the original title and the reformulated title. Then we …

Log-Based Anomaly Detection Based On Evt Theory With Feedback, Jinyang Liu, Junjie Huang, Yintong Huo, Zhihan Jiang, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Minzhi Yan, R. Michael Lyu

Research Collection School Of Computing and Information Systems

System logs play a critical role in maintaining the reliability of software systems. Fruitful studies have explored automatic log-based anomaly detection and achieved notable accuracy on benchmark datasets. However, when applied to large-scale cloud systems, these solutions face limitations due to high resource consumption and lack of adaptability to evolving logs. In this paper, we present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as SeaLog. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA's accuracy in response to evolving log data, we enable …

Are We Ready To Embrace Generative Ai For Software Q&A?, Bowen Xu, Thanh-Dat Nguyen, Thanh Le Cong, Thong Hoang, Jiakun Liu, Kisub Kim, Chen Gong, Changan Niu, Chenyu Wang, Xuan-Bach Dinh Le, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, …

Are We Ready To Embrace Generative Ai For Software Q&A?, Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong, Thong Hoang, Jiakun Liu, Kisub Kim, Chen Gong, Changan Niu, Chenyu Wang, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, …

Learning Representations For Effective And Explainable Software Bug Detection And Fixing, Yi Li

Dissertations

Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a 'bridge,' known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning.

This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning …

Ide-Based Learning Analytics For Assessing Introductory Programming Skill, Phyllis J. Beck

Theses and Dissertations

Providing a sufficient level of personalized feedback on students' current level of strategic knowledge within the context of the natural programming environment through IDE-based learning analytics would transform learning outcomes for introductory programming students. However, providing sufficient insight into the programming process was previously inaccessible due to the need for more complex and scalable data collection methods and metrics with a wider variety for understanding programming metacognition and the full programming process.

This research developed a custom-built web-based IDE and event compression system to investigate two of the five components of a five-dimensional model of cognition for programming skill estimation …

Semantic-Based Neural Network Repair, Richard Schumi, Jun Sun

Research Collection School Of Computing and Information Systems

Recently, neural networks have spread into numerous fields including many safety-critical systems. Neural networks are built (and trained) by programming in frameworks such as TensorFlow and PyTorch. Developers apply a rich set of pre-defined layers to manually program neural networks or to automatically generate them (e.g., through AutoML). Composing neural networks with different layers is error-prone due to the non-trivial constraints that must be satisfied in order to use those layers. In this work, we propose an approach to automatically repair erroneous neural networks. The challenge is in identifying a minimal modification to the network so that it becomes valid. …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Algorithmic Bias: Causes And Effects On Marginalized Communities, Katrina M. Baha

Undergraduate Honors Theses

Individuals from marginalized backgrounds face different healthcare outcomes due to algorithmic bias in the technological healthcare industry. Algorithmic biases, which are the biases that arise from the set of steps used to solve or analyze a problem, are evident when people from marginalized communities use healthcare technology. For example, many pulse oximeters, which are the medical devices used to measure oxygen saturation in the blood, are not able to accurately read people who have darker skin tones. Thus, people with darker skin tones are not able to receive proper health care due to their pulse oximetry data being inaccurate. This …

Ptmtorrent: A Dataset For Mining Open-Source Pre-Trained Model Packages, Wenxin Jiang, Nicholas Synovic, Purvish Jajal, Taylor R. Schorlemmer, Arav Tewari, Bhavesh Pareek, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing …

Trustworthy And Synergistic Artificial Intelligence For Software Engineering: Vision And Roadmaps, David Lo

Research Collection School Of Computing and Information Systems

For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engiueering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to …

Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. …

What Do Users Ask In Open-Source Ai Repositories? An Empirical Study Of Github Issues, Zhou Yang, Chenyu Wang, Jieke Shi, Thong Hoang, Pavneet Singh Kochhar, Qinghua Lu, Zhenchang Xing, David Lo

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, and image processing. The advancement of these AI systems is inseparable from open-source software (OSS). Specifically, many benchmarks, implementations, and frameworks for constructing AI systems are made open source and accessible to the public, allowing researchers and practitioners to reproduce the reported results and broaden the application of AI systems. The development of AI systems follows a data-driven paradigm and is sensitive to hyperparameter settings and data separation. Developers …

Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …

Designing An Equity-Centered Framework And Crosswalk For Integrated Elementary Computer Science Curriculum And Instruction, Carla Strickland, Andrea Ramírez-Salgado, Lauren Weisberg, Latoya Chandler, Jeanne Di Domenico, Elizabeth M. Lehman, Maya Israel

Journal of Computer Science Integration

As computer science (CS) education becomes more prevalent in K-12 instruction, it is critical for educators, researchers, and curriculum developers to identify culturally responsive and pedagogically inclusive approaches that can increase participation, access, and feelings of belonging for students from historically marginalized communities. In response, we developed an equity-centered curricular framework and illustrative crosswalk that synchronizes three distinct pedagogical approaches: culturally responsive pedagogy (CRP), Universal Design for Learning (UDL), and project-based learning (PBL). We describe the framework’s theoretical underpinnings and explain how this framework informed the development of an integrated elementary science+CS curricular unit and provide examples of its implementation. …

Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe

[Archive] Belmont University Research Symposium (BURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …

Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermudez I Badia

Research Collection School Of Computing and Information Systems

Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these systems still remains a challenge. In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation. Through interviews with therapists, we designed how this system interacts with the user and then developed an interactive …

Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Object detection, while being an attractive interaction method for Augmented Reality (AR), is fundamentally error-prone due to the probabilistic nature of the underlying AI models, resulting in sub-optimal user experiences. In this paper, we explore the effect of three game design concepts, Ambiguity, Transparency, and Controllability, to provide better gameplay experiences in AR games that use error-prone object detection-based interaction modalities. First, we developed a base AR pet breeding game, called Bubbleu that uses object detection as a key interaction method. We then implemented three different variants, each according to the three concepts, to investigate the impact of each design …

A Data Augmented Method For Plant Disease Leaf Image Recognition Based On Enhanced Gan Model Network, Mingyuan Xin, Ling Weay Ang, Sellappan Palaniappan

Journal of Informatics and Web Engineering

The identification of plant disease leaves based on deep learning is the key to control the development and spread of plant diseases. In this paper, the existing problems of traditional classification and recognition of plant disease leaves and the limitations of deep learning-based plant disease leaf training are analysed. An enhanced GAN model network based on the Wasserstein GAN loss function has been developed to address the limited training images of plant disease leaves. The self-attention layer is added into the self-encoding structure of the generating network. The effectiveness of data generated by the encoder is increased after the self-attention …

Elderly And Smartphone Apps: Case Study With Lightweight Mysejahtera, Ciu Yung Seek, Shih Yin Ooi, Ying Han Pang, Sook Ling Lew, Xin Yun Heng

Journal of Informatics and Web Engineering

The outbreak of Covid-19 in the past 2 years has made the usage of contact-tracing app almost mandatory in Malaysia. Though the usage is seeming to be simple, but one interesting phenomenon can be observed in Malaysia is that most of the senior citizen are found not using smartphones regularly. While check-in through MySejahtera (the primary contract-tracing app used in Malaysia) for business, premises, and transports has made compulsory, it does bring a lot of inconveniences to the elderly. Many elderlies start to learn how to use smartphone and government has also taking initiative by providing free or low-cost smartphone …

The Impact Of The Telecommunication Industry As A Moderator On Poverty Alleviation And Educational Programmes To Achieve Sustainable Development Goals In Developing Countries, S.K.C Ruklani Wickramasinghe, Kamal Abd Razak

Journal of Informatics and Web Engineering

The purpose of this study is to investigate the influence of the telecommunications industry's contribution to sustainable development in developing nations. A detailed literature study is conducted for this purpose, and a conceptual framework is offered. To validate the proposed conceptual framework, we conducted multiple case studies involving companies in the telecommunications industry from various countries. These findings improved the suggested conceptual framework produced for this study. However, despite the contributions of the telecommunications industry to the Sustainable Development Goals, no study has been conducted to examine how they have benefited poverty reduction and educational programmes in Sri Lanka. The …

A Swot Analysis With A Digital Transformation: A Case Study For Hospitals In The Pharmaceutical Supply Chain, Nguyen Huu Khanh Quan, Harwindar Singh, Thai Hong Thuy Khanh, Premkumar Rajagopal

Journal of Informatics and Web Engineering

The pharmaceutical business meets all requirements for a complicated and tightly regulated sector, including multi-stakeholder participation. The objective is to study digital transformation strategies in hospitals to improve the safety and convenience of pharmaceutical services when providing healthcare services. In this study, a SWOT analysis is performed to identify strengths, weaknesses, opportunities, and threats with a digital transformation case study for hospitals in the pharmaceutical supply chain in Ho Chi Minh City, Vietnam. Information source for SWOT analysis of published articles and reports in the following aspects: Digital transformation environment of hospitals in the global pharmaceutical supply chain; new technology …

Enhancing Mimo Capacity Through Space-Time Coding: Analysis And Design Framework, Lijun Han, Ling Weay Ang, Sellappan Palaniappan, Jing Wang

Journal of Informatics and Web Engineering

Space-time coding combines time and space to generate codewords, transmitting signals in both time and space domains. This leads to not only diversity and coding gains but also reduces the impact of multipath fading, resulting in high spectral efficiency. This paper examines the challenges in implementing space-time coding to enhance the capacity of MIMO systems. It analyzes the principle, design objectives, and criteria of space-time coding, providing a basic design framework, based on the space-time coding (STC) system model. .STC and MIMO have proven to be effective in improving system capacity, reliability, and overall performance in wireless communication. They have …

Robust Image Watermarking With Quaternion Fractional-Order Polar Harmonic-Fourier Moments Based On Wavelet Transformation: Resistance Against Rotation Attacks, Wang Jing, Ling Weay Ang, Sellappan Palaniappan, Bing He

Journal of Informatics and Web Engineering

This study presents a zero-watermarking algorithm that can resist rotation attacks. The algorithm uses quaternion fractional-order polar harmonic-Fourier moments (QFr-PHFMs) based on wavelet-transformation. First, the wavelet-transformation is applied to each component of the host image, which is in RGB three-channel color. The low-frequency sub-bands of each component are then extracted and represented using quaternion algebra. Multiple QFr-PHFMs are calculated, and the invariants of the QFr-PHFMs are utilized to establish the watermark system. The watermark extraction process is also simplified. The detection of the image requires a two-level wavelet transformation, followed by the calculation of multiple invariant moments of the low-frequency …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Conversations With Chatgpt About C Programming: An Ongoing Study, James C. Davis, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

AI (Artificial Intelligence) Generative Models have attracted great attention in recent years. Generative models can be used to create new articles, visual arts, music composition, even computer programs from English specifications. Among all generative models, ChatGPT is becoming one of the most well-known since its public announcement in November 2022. GPT means {\it Generative Pre-trained Transformer}. ChatGPT is an online program that can interact with human users in text formats and is able to answer questions in many topics, including computer programming. Many computer programmers, including students and professionals, are considering the use of ChatGPT as an aid. The quality …

The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang

Research Collection School Of Computing and Information Systems

This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total …

Comparing Boys’ And Girls’ Attitudes Toward Computer Science, Danielle Scott, Amiee Zou, Sharin Rawhiya Jacob, Debra Richardson, Mark Warschauer

Journal of Computer Science Integration

Women are severely underrepresented in computer science (CS) degrees and careers. While student interest is a key predictor of success, little is known about how elementary students from underserved groups, such as girls, develop their interest in CS. To address this issue, we examined the differences in attitudes between upper elementary girls and boys towards CS after participating in a yearlong, inquiry-based CS curriculum designed for diverse learners. Pre-and-post surveys on students’ attitudes towards CS (n = 108) were delivered before and after student participation in the curriculum. Results from the survey showed only two demonstrated significant differences between boys …

An Enhanced Cloud-Native Deep Learning Pipeline For The Classification Of Network Traffic, Ahmed Sobhy Elkenawy

Theses and Dissertations

In a rapidly changing world, the way of solving real-world problems has changed to leverage the power of the advancements in multiple fields. Cloud-native computing approaches can be utilized with deep learning techniques to provide solutions in several important areas. For instance, with the emergence of the pandemic, much dependence on modern technologies came out as a replacement for face-to-face interaction. Deep learning can reach a high level of accuracy, which makes it very effective in the support of modern services and technologies. However, there are some challenging issues because deep learning requires many large-scale experiments, which demand a lot …

Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Future particle accelerators will exceed by far the current data size (10¹⁵) per experiment, and high- luminosity program(s) will produce more than 300 times as much data. Classical Machine Learning (ML) likely will benefit from new tools based on quantum computing. Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. A combinatorial approach exhaustively tests track measurements (“hits”), represented as images, to identify those that form an actual particle trajectory, which is then used to reconstruct track parameters necessary for the physics experiment. Quantum Machine Learning (QML) could improve this process in multiple ways, …