Software Engineering Commons^™

Partial Solution Based Constraint Solving Cache In Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Kelin Ma, Kunlin Liu, Yufeng Zhang, Jun Sun, Ji Wang

Research Collection School Of Computing and Information Systems

Constraint solving is one of the main challenges for symbolic execution. Caching is an effective mechanism to reduce the number of the solver invocations in symbolic execution and is adopted by many mainstream symbolic execution engines. However, caching can not perform well on all programs. How to improve caching’s effectiveness is challenging in general. In this work, we propose a partial solution-based caching method for improving caching’s effectiveness. Our key idea is to utilize the partial solutions inside the constraint solving to generate more cache entries. A partial solution may satisfy other constraints of symbolic execution. Hence, our partial solution-based …

On The Sustainability Of Deep Learning Projects: Maintainers' Perspective, Junxiao Han, Jiakun Liu, David Lo, Chen Zhi, Yishan Chen, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers' workloads and workloads growth in DL projects, and compare them with traditional open-source software (OSS) projects. In this regard, we first investigate …

Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Conceptualising and debugging a microservice architecture can be a challenge for developers due to the complex topology of inter-service communication, which may only apparent when viewing the architecture as a whole. In this paper, we present MicroKarta, a dashboard containing three types of network diagram that visualise complex microservice architectures, and that are designed to address problems faced by developers of these architectures. Initial feedback from industry developers has been positive. This dashboard can be used by developers to explore and debug microservice architectures, and can be used to compare the effectiveness of different types of network visualisation for assisting …

Toward Effective Secure Code Reviews: An Empirical Study Of Security-Related Coding Weaknesses, Wachiraphan Charoenwet, Patanamon Thongtanunam, Thuan Pham, Christoph Treude

Research Collection School Of Computing and Information Systems

Identifying security issues early is encouraged to reduce the latent negative impacts on software systems. Code review is a widely-used method that allows developers to manually inspect modified code, catching security issues during a software development cycle. However, existing code review studies often focus on known vulnerabilities, neglecting coding weaknesses, which can introduce real-world security issues that are more visible through code review. The practices of code reviews in identifying such coding weaknesses are not yet fully investigated. To better understand this, we conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code …

Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adoption and its impact on the development process. Additionally, it offers a unique opportunity to observe how developers respond when they disagree with the generated content. In our study, we employ a mixed-methods approach, blending quantitative analysis with qualitative insights, to examine 18,256 PRs in …

Certified Robust Accuracy Of Neural Networks Are Bounded Due To Bayes Errors, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Adversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably. Despite various proposals for addressing this issue, the significant accuracy drop remains. More importantly, it is not clear whether there is a certain fundamental limit on achieving robustness whilst maintaining accuracy. In this work, we offer a novel perspective based on Bayes errors. By adopting Bayes error to robustness analysis, we investigate the limit of certified robust accuracy, taking into account data distribution uncertainties. We first show that the accuracy inevitably decreases in the pursuit of …

Prioritising Github Priority Labels, James Caddy, Christoph Treude

Research Collection School Of Computing and Information Systems

Communities on GitHub often use issue labels as a way of triaging issues by assigning them priority ratings based on how urgently they should be addressed. The labels used are determined by the repository contributors and notstandardisedbyGitHub.Thismakes it difficult for priority-related reasoning across repositories for both researchers and contributors. Previous work shows interest in how issues are labelled and what the consequences for those labels are. For instance, some previous work has used clustering models and natural language processing to categorise labels without a particular emphasis on priority. With this publication, we introduce a unique data set of 812 manually …

Reproducibility Debt: Challenges And Future Pathways, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility of scientic computation is a critical factor in validating its underlying process, but it is often elusive. Complexity and continuous evolution in software systems have introduced new challenges for reproducibility across a myriad of computational sciences, resulting in growing debt. This requires a comprehensive domain-agnostic study to dene and asses Reproducibility Debt (RpD) in scientic software, thus uncovering and classifying all underlying factors attributed towards its emergence and identication i.e., causes and eects. Moreover, an organised map of prevention strategies is imperative to guide researchers for its proactive management. This vision paper highlights the challenges that hinder eective management …

How Do Preservice Teachers Learn To Teach Integrated Computational Thinking?: Evidence From Planning, Enactment, And Reflection, Rachael Dektor, Samuel Severance, Kip Téllez

Journal of Computer Science Integration

This study examines pre-service teachers’ (PSTs) beliefs and understandings about computational thinking (CT) integration and lesson implementation over time. Utilizing a design-based research approach, 3 PSTs led the co-design of integrated CT lessons with support from researchers and enacted these CT integrated lessons with K-5 students. All PSTs participated in a whole-group CT workshop and engaged in one-on-one lesson design sessions with a researcher. We utilized a grounded theory approach to qualitatively analyze pre-surveys, semi-structured interviews, and video data of three PSTs enacting their lessons. We found that PSTs’ initial beliefs about CT instruction – including the importance of it …

Henna Chatbot Capstone Review, Kobe Norcross

University Honors Theses

This thesis reviews the development of the Henna Chatbot, an AI-powered DEI consultant designed to provide personalized feedback to organizations. Sponsored by DEI consultant Arsh Haque, the project aims to address gaps in current DEI software, which often lacks team-specific feedback. The Henna Chatbot leverages GPT-3.5 Turbo to create an affordable SaaS platform where organizations can train Henna with their DEI values, and Henna will help organizations stay aligned with those values. The project spanned twenty weeks and was completed by a team of eight computer science students at Portland State University. The development process followed Agile methodologies, emphasizing effective …

Trust, Transparency, And Transport: The Impact Of Privacy Protection On The Acceptance Of Last-Mile Drone Delivery, Jurgen Heinz Famula

Electronic Theses and Dissertations

A common set of problems commercial delivery companies face is finding ways to increase the efficiency and reliability of the “last mile” of a package’s journey, all while reducing operating costs. This need for efficiency has driven many companies to explore using unmanned aerial vehicles (UAVs), or drones, to get packages to their final destination. Although UAVs have great potential to help increase efficiency in commercial package delivery, this comes at a potential cost to the privacy of people who intersect the flight paths of these unmanned vehicles. This thesis explores the effect of a mobile phone application for commercial …

Evaluating The Effect Of Domain-Specific Large Language Models On Question And Response, Phillip D. Crippen

Electronic Theses and Dissertations

Large Language Models have emerged to great fanfare in the Information Technology market. Business and Information Technology leaders are currently exploring ways to apply these models to assist their organizations in executing business processes and generating innovation. Software vendors, consultants, and academics promote various approaches to making Large Language Models work effectively for business. However, little academic literature is available today that quantifies the degree of improvement possible with these domain-specific approaches over the standard capabilities of generalized Large Language Models.

The study seeks to quantify the benefits of one approach, Retrieval Augmented Generation. The study uses a collection of …

Cards With Class: Formalizing A Simplified Collectible Card Game, Dan Ha

University Honors Theses

Collectible card games (CCGs) have been a wildly popular game genre since the release of Wizards of the Coast's Magic: The Gathering. These games revolve around their thousands of cards and the hundreds of thousands of interactions they can create with their many effects. For designers, it is an incredibly demanding task to ensure that every single card works properly and that each card's text unambiguously conveys its intended behavior in all cases. The task only grows more difficult over time as the number of cards in the game grows and card effects become more complex or experimental. If the …

The Institutional Challenges Of A Quantified Self Study: An Attempt To Ascertain How Data Collected From A Mobile Device Can Be An Indicator Of Personal Mental Health Over Time, Julian Lazaras

University Honors Theses

The adoption of an application of new technology always comes with a bias, this is never more true for the case of human behavioral analytics within higher education. While movements such as the quantified self movement make strides to reinterpret the realm of data analytics, psychology, and computer science, there are inevitably limitations to the adoption and application of such approaches within the standard realm of research. Herein is presented a case where an effort to evaluate the prospect of use of mobile phone data as secondary indicators of personal mental health through the lens of data analysis was put …

Real Time 3d Internal Building Directory Map, Zi Yang Chia, Pey Yun Goh

Journal of Informatics and Web Engineering

Global Positioning System (GPS) is a famous technology around the world in identifying the real time precise location of any object with the assistance of satellites. The most common application of GPS is the use of outdoor maps. GPS offers efficient, scalable and cost-effective location services. However, this technology is not reliable when the position is in an indoor environment. The signal is very weak or totally lost due to signal attenuation and multipath effects. Among the indoor positioning technologies, WLAN is the most convenient and cost effective. In recent research, machine learning algorithms have become popular and utilized in …

Development And Validation Of Autotronic Training Module For Automobile Technology Students In Polytechnics In Southern Nigeria, Saue, Baritule Prince, Chukuigwe, Ogbondah Nndameka, Bassey, Imaobong Sunday

Journal of Informatics and Web Engineering

A research was carried out to create and verify an autotronic training module for students studying vehicle technology at polytechnics located in Southern Nigeria. The design used for the project was Research and Development (R&D). An observation has been made that the curriculum of polytechnics in Nigeria lacks sufficient substance on autotronics technology. As a result, lecturers have challenges in fully imparting the abilities that are essential for the professional world. Undoubtedly, there is now a disparity between the training that craftsmen receive and the skills that are demanded by industries. The research was carried out in the southern region …

Ensemble-Smote: Mitigating Class Imbalance In Graduate On Time Detection, Theng-Jia Law, Choo-Yee Ting, Hu Ng, Hui-Ngo Goh, Albert Quek

Journal of Informatics and Web Engineering

In education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class imbalance in the dataset. This study has aimed to (i) compare various class imbalance treatment methods with different sampling ratios, (ii) propose an ensemble class imbalance treatment method in mitigating the problem of class imbalance, and (iii) develop and evaluate predictive models in identifying the likelihood of students graduating on time during their studies in university. The dataset is collected from 4007 graduates of …

Facial Skin Analysis In Malaysians Using Yolov5: A Deep Learning Perspective, Ying Huey Gan, Shih Yin Ooi, Ying Han Pang, Yi Hong Tay, Quan Fong Yeo

Journal of Informatics and Web Engineering

Nowadays, people are more concerned about their skin conditions and are more willing to spend money and time on facial care routines. The beauty sector market is increasing, and more skin type readers are being created to help people determine their skin type. While various skin type readers are in the market, each is invented and tested abroad. Those skin type readers in the beauty market are not applied well on Malaysian skin. Therefore, this paper proposes a facial skin analysis system tailored primarily for Malaysian skin. This paper integrated object detection and deep learning algorithms in developing skin-type readers. …

Assessing The Efficiency Of Deep Learning Methods For Automated Vehicle Registration Recognition For University Entrance, Muhammad Syaqil Irsyad, Zarina Che Embi, Khairil Imran Bin Ghauth

Journal of Informatics and Web Engineering

With the ever-increasing number of vehicles on the road, a faster reliable security system for university entry is needed. This paper presents an approach for Automatic Number Plate Recognition (ANPR) using deep learning and PP-OCRv3. The proposed approach utilizes a pre-trained object detection model to locate license plates, extracts a single frame of the license plate, performs license plate recognition, applies pre-processing techniques, and employs PP-OCRv3 for text extraction in real time. The system was tested with Malaysian vehicle plates, and its accuracy and speed of detection were evaluated. The results show the system's potential to be easily adapted to …

Secure Room-Sharing Decentralized App Development On Ethereum Block Chain Using Smart Contracts, Hasnain Raza, Reqad Ali, Jawaid Iqbal, Muhammad Awais

Journal of Informatics and Web Engineering

The purpose of this research is to analyze whether Blockchain technology can affect the share-economy. Apart from that, blockchain technology has been innovating the whole of the industries and so the academics are discovering the possibilities and starting to incorporate them in order to provide additional tech possibilities. The sharing economic system is the system which enables to share asset among the one person to the other person. It has seen the remarkable growth in the last few years, Uber, Careem, Airbnb, Zostel, Hostel World are some companies to mention which have fueled this growth. Yet, the majority of the …