Integrating Blockchain Technology Into The Software Development Life Cycle To Satisfy The Software Bill Of Materials Requirement For Government Software Systems,
2024
Air Force Institute of Technology
Integrating Blockchain Technology Into The Software Development Life Cycle To Satisfy The Software Bill Of Materials Requirement For Government Software Systems, Walter T. Scott Ii
Theses and Dissertations
This thesis explores the integration of Blockchain Technology (BT) into the Software Development Life Cycle (SDLC) to satisfy the Software Bill of Materials (SBOM) requirement for government software systems. This study begins by synthesizing a standard SDLC definition from various government and industry references, which may provide the foundation for future efforts to standardize software development practices across the government software development community. This study proceeds to define working definitions for the software supply chain (SSC) and software supply chain management (SCM) before introducing and detailing the SBOM requirement as well as providing an overview of prior research regarding SBOMs …
Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction,
2024
Singapore Management University
Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu
Research Collection School Of Computing and Information Systems
This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby …
An Empirical Study Of Static Analysis Tools For Secure Code Review,
2024
Singapore Management University
An Empirical Study Of Static Analysis Tools For Secure Code Review, Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude
Research Collection School Of Computing and Information Systems
Early identification of security issues in software development is vital to minimize their unanticipated impacts. Code review is a widely used manual analysis method that aims to uncover security issues along with other coding issues in software projects. While some studies suggest that automated static application security testing tools (SASTs) could enhance security issue identification, there is limited understanding of SAST’s practical effectiveness in supporting secure code review. Moreover, most SAST studies rely on synthetic or fully vulnerable versions of the subject program, which may not accurately represent real-world code changes in the code review process. To address this gap, …
Meta-Learning For Multi-Family Android Malware Classification,
2024
Singapore Management University
Meta-Learning For Multi-Family Android Malware Classification, Yao Li, Dawei Yuan, Tao Zhang, Haipeng Cai, David Lo, Cuiyun Gao, Xiapu Luo, He Jiang
Research Collection School Of Computing and Information Systems
With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious …
Ai Coders Are Among Us : Rethinking Programming Language Grammar Towards Efficient Code Generation,
2024
Singapore Management University
Ai Coders Are Among Us : Rethinking Programming Language Grammar Towards Efficient Code Generation, Sun Zhensu, Du Xiaoning, Yang Zhou, Li Li, David Lo
Research Collection School Of Computing and Information Systems
Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models (LLMs). LLMs can now perform well in coding competitions and even write programs like developers to solve various tasks, including mathematical problems. However, the grammar and layout of current programs are designed to cater the needs of human developers -- with many grammar tokens and formatting tokens being used to make the code easier for humans to read. While this is helpful, such a design adds unnecessary computational work for LLMs, as each token …
Evaluating Szz Implementations : An Empirical Study On The Linux Kernel,
2024
Singapore Management University
Evaluating Szz Implementations : An Empirical Study On The Linux Kernel, Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo
Research Collection School Of Computing and Information Systems
The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as “ghost commits”. The evaluation of how these ghost commits impact the SZZ implementations remains limited. Moreover, these implementations have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the …
Face It Yourselves: An Llm-Based Two-Stage Strategy To Localize Configuration Errors Via Logs,
2024
Singapore Management University
Face It Yourselves: An Llm-Based Two-Stage Strategy To Localize Configuration Errors Via Logs, Shiwen Shi, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng
Research Collection School Of Computing and Information Systems
Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause …
Real Time Pii Scanning,
2024
University of Denver
Real Time Pii Scanning, John David
Electronic Theses and Dissertations
The increased amount of web applications and internet software solutions utilizing cloud frameworks has contributed to large data sets of system log messages being generated constantly. These messages may contain sensitive data, creating an additional security risk for the systems and contributing to the need for analysis of such large volumes of data in real time. Large commercial data monitoring systems can solve for these analysis requirements, but they can be costly. We present a solution to analyzing web application log data which ingests it, processes it and visualizes sensitive data found within in real time. Our solution utilizes an …
Engineering Ecological Analysis Of Rise Of Huawei’S Harmonyos And Its Implications,
2024
School of Humanities, University of Chinese Academy of Sciences, Beijing 100049, China; China State Railway Group Co. Ltd., Beijing 100080, China
Engineering Ecological Analysis Of Rise Of Huawei’S Harmonyos And Its Implications, Dazhou Wang, Yishi Lyu, Zhihuan Fu
Bulletin of Chinese Academy of Sciences (Chinese Version)
This study aims to explore the rise of the Harmony operating system from the perspective of engineering ecology. In the face of increasingly fierce global technological competition, Huawei, as a leading Chinese information technology enterprise, launched its self-developed HarmonyOS and has progressively built the Harmony ecosystem, providing foundational support for the development of all sectors. The development path of HarmonyOS can be divided into three main phases based on its strategic goals, technological and product characteristics, and ecological construction: the initial phase, the acceleration phase, and the transformation phase. It is revealed that, throughout this development, various construction strategies for …
Challenges And Practices Of Deep Learning Model Reengineering: A Case Study On Computer Vision,
2024
Purdue University
Challenges And Practices Of Deep Learning Model Reengineering: A Case Study On Computer Vision, Wenxin Jiang, Vishnu Banna, Naveen Vivek, Abhinav Goel, Nicholas Synovic, George K. Thiruvathukal, James C. Davis
Computer Science: Faculty Publications and Other Works
Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Deep learning model reengineering — reusing, replicating, adapting, and enhancing state-of-the-art deep learning approaches — is challenging for reasons including under-documented reference models, changing requirements, and the cost of implementation and testing.
Reless: A Framework For Assessing Safety In Deep Learning Systems,
2024
CUNY Graduate Center
Reless: A Framework For Assessing Safety In Deep Learning Systems, Nan Jia, Anita Raja, Raffi T. Khatchadourian
Publications and Research
Traditionally, software refactoring helps to improve a system's internal structure and enhance its non-functional features, such as reliability and run-time performance, while preserving external behavior including original program semantics. However, in the context of learning-enabled software systems (LESS), e.g., Machine Learning (ML) systems, it is unclear which portions of a software's semantics require preservation at the development phase. This is mainly because (a) the behavior of the LESS is not defined until run-time; and (b) the inherently iterative and non-deterministic nature of ML algorithms. Consequently, there is a knowledge gap in what refactoring truly means in the context of LESS …
Feature Importance In The Context Of Traditional And Just-In-Time Software Defect Prediction Models,
2024
Fanshawe College of Applied Arts and Technology
Feature Importance In The Context Of Traditional And Just-In-Time Software Defect Prediction Models, Susmita Haldar, Luiz Fernando Capretz
Electrical and Computer Engineering Publications
Software defect prediction models can assist software testing initiatives by prioritizing testing error-prone modules. In recent years, in addition to the traditional defect prediction model approach of predicting defects from class, modules, etc., Just-In- Time defect prediction research, which focuses on the change history of software products is getting prominent. For building these defect prediction models, it is important to understand which features are primary contributors to these classifiers. This study considered developing defect prediction models incorporating the traditional and the Just-In-Time approaches from the publicly available dataset of the Apache Camel project. A multi-layer deep learning algorithm was applied …
Materials Data Science Ontology (Mds-Onto): Unifying Domain Knowledge In Materials And Applied Data Science,
2024
Case Western Reserve University
Materials Data Science Ontology (Mds-Onto): Unifying Domain Knowledge In Materials And Applied Data Science, Van D. Tran, Jonathan E. Gordon, Alexander Harding Bradley, Balashanmuga Priyan Rajamohan, Quynh D. Tran, Gabriel Ponón, Yinghui Wu, Laura S. Bruckman, Erika I. Barcelos, Roger H. French
Student Scholarship
Ontologies have gained popularity in the scientific community as a means of standardizing concepts and terminology used in metadata across different institutions to facilitate data comprehension, sharing, and reuse. Despite the existence of frameworks and guidelines for building ontologies, the processes and standards used to develop ontologies still differ significantly, particularly in Materials Science. Our goal with the MDS-Onto Framework is to provide a unified and automated system for ontology development in the Materials and Data Sciences. This framework offers recommendations on where to publish ontologies online, how to best integrate them within the semantic web, and which formats to …
Neural Network Semantic Backdoor Detection And Mitigation: A Causality-Based Approach,
2024
Singapore Management University
Neural Network Semantic Backdoor Detection And Mitigation: A Causality-Based Approach, Bing Sun, Jun Sun, Wayne Koh, Jie Shi
Research Collection School Of Computing and Information Systems
Different from ordinary backdoors in neural networks which are introduced with artificial triggers (e.g., certain specific patch) and/or by tampering the samples, semantic backdoors are introduced by simply manipulating the semantic, e.g., by labeling green cars as frogs in the training set. By focusing on samples with rare semantic features (such as green cars), the accuracy of the model is often minimally affected. Since the attacker is not required to modify the input sample during training nor inference time, semantic backdoors are challenging to detect and remove. Existing backdoor detection and mitigation techniques are shown to be ineffective with respect …
A New Hope: Contextual Privacy Policies For Mobile Applications And An Approach Toward Automated Generation,
2024
Singapore Management University
A New Hope: Contextual Privacy Policies For Mobile Applications And An Approach Toward Automated Generation, Shidong Pan, Zhen Tao, Thong Hoang, Dawen Zhang, Tianshi Li, Zhenchang Xing, Xiwei Xu, Mark Staples, Thierry Rakotoarivelo, David Lo
Research Collection School Of Computing and Information Systems
Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application’s graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding …
Exponential Qubit Reduction In Optimization For Financial Transaction Settlement,
2024
Singapore Management University
Exponential Qubit Reduction In Optimization For Financial Transaction Settlement, Elias X. Huber, Benjamin Y. L. Tan, Paul Robert Griffin, Dimitris G. Angelakis
Research Collection School Of Computing and Information Systems
We extend the qubit-efficient encoding presented in (Tan et al. in Quantum 5:454, 2021) and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also …
Remote Onboarding Of Software Developers: Leveraging Virtual Reality And Ai Tools,
2024
Clemson University
Remote Onboarding Of Software Developers: Leveraging Virtual Reality And Ai Tools, James Dominic
All Dissertations
Software development teams add newcomers to accommodate the increasing demand, complexity of software solutions, and turnover. Onboarding newcomers is expensive and error-prone. It can take up to three years for a newcomer to become an expert on a project. Onboarding techniques described in the current literature focus on collocated teams. As more teams are adopting remote and distributed team structures, I address this research gap in understanding remote onboarding for software developers. I present my research on the use of Virtual Reality (VR) for remote software developer onboarding. I discuss a VR remote pair programming environment. With positive outcomes, pair …
Vysion Software,
2024
Eastern Washington University
Vysion Software, Isaias Hernandez-Dominguez Jr, Chander Luderman Miller
2024 Symposium
Vision loss presents significant challenges in daily life. Existing solutions for blind and visually impaired individuals are often limited in functionality, expensive, or complex to use. Vysion Software addresses this gap by developing a user-friendly, all-in-one AI companion app that provides features including text summarization, real-time audio descriptions, and AI-enhanced navigation. This project details the development plan, initial functionalities, and future vision for Vysion Software.
Microservices Architecture: Evolution, Realizing Benefits, And Addressing Challenges In The Modern Software Era -A Systematic Literature Review,
2024
future university in egypt
Microservices Architecture: Evolution, Realizing Benefits, And Addressing Challenges In The Modern Software Era -A Systematic Literature Review, Linah M. Elnaghi, Ramadan Moawad
Future Computing and Informatics Journal
This paper explores the world of modern software development and the rising popularity of microservices architecture. Microservices, a modern approach, brings benefits like scalability, Reusability, and fault tolerance. challenging traditional monolithic approaches.This survey involves a detailed comparison, unraveling the motivations behind the wide usage of microservices. This paper extracts insights from a diverse range of studies, presenting a clear and accessible synthesis of the key benefits and challenges associated with microservices architecture. Through a methodical analysis of these factors, the study aims to discern the most pivotal advantages and challenges within the domain of microservices. Steering away from complicated terminology, …
Peatmoss: A Dataset And Initial Analysis Of Pre-Trained Models In Open-Source Software,
2024
Purdue University
Peatmoss: A Dataset And Initial Analysis Of Pre-Trained Models In Open-Source Software, Wenxin Jiang, Jerin Yasmin, Jason Jones, Nicholas Synovic, Jiashen Kuo, Nathaniel Bielanski, Yuan Tian, George K. Thiruvathukal, James C. Davis
Computer Science: Faculty Publications and Other Works
The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with …
