Advancing Devsecops In Smes: Challenges And Best Practices For Secure Ci/Cd Pipelines,
2025
Dakota State University
Advancing Devsecops In Smes: Challenges And Best Practices For Secure Ci/Cd Pipelines, Jayaprakashreddy Cheenepalli, John Hastings, Khandaker Mamun Ahmed, Chad Fenner
Research & Publications
This study evaluates the adoption of DevSecOps among small and medium-sized enterprises (SMEs), identifying key challenges, best practices, and future trends. Through a mixed methods approach backed by the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, we analyzed survey data from 405 SME professionals, revealing that while 68% have implemented DevSecOps, adoption is hindered by technical complexity (41%), resource constraints (35%), and cultural resistance (38%). Despite strong leadership prioritization of security (73%), automation gaps persist, with only 12% of organizations conducting security scans per commit. Our findings highlight a growing integration of security tools, particularly API security …
Can Llms Replace Manual Annotation Of Software Engineering Artifacts?,
2025
Singapore Management University
Can Llms Replace Manual Annotation Of Software Engineering Artifacts?, Toufique Ahmed, Premkumar Devanbu, Christoph Treude, Michael Pradel
Research Collection School Of Computing and Information Systems
Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain greater generalizability of the findings. However, human-subject studies in our field are challenging, due to the cost and difficulty of finding and employing suitable subjects, ideally, professional programmers with varying degrees of experience. Meanwhile, large language models (LLMs) have recently started to demonstrate human-level performance in several areas. This paper explores the possibility of substituting costly human subjects with much cheaper LLM queries in evaluations of code and code-related artifacts. We study this idea by applying six …
Prioritizing Speech Test Cases,
2025
Singapore Management University
Prioritizing Speech Test Cases, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, Bowen Xu, Xin Zhou, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
As Automated Speech Recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-consuming. This presents a compelling case for the strategic prioritization of speech test cases, which consist of a piece of audio and the corresponding reference text. The central question we address is: In what sequence should speech test cases be collected and executed to identify the maximum number of errors at the earliest stage? In this study, we introduce PRiOritizing sPeecH tEsT …
Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions,
2025
Singapore Management University
Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions, T.Y. Zhuo, M.C. Vu, J. Chim, ..., David Lo
Research Collection School Of Computing and Information Systems
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately …
Use Of Search Tools In Software Development: A Study Of Microservice-Based Team Projects,
2025
Singapore Management University
Use Of Search Tools In Software Development: A Study Of Microservice-Based Team Projects, Yi Meng Lau, Christian Michael Koh, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Universities are increasingly integrating real-world projects into software engineering curricula to preparestudents for careers involving complex concepts like Microservices Architecture (MSA). Students frequentlystruggle with such concepts within limited class time and turn to various search tools and online resources for additional help. Search tools are also widely used in the software development industry. While search engines, like Google and Yahoo!, can provide quick solutions, they pose the risk of information overload. Large Language Models (LLMs) such as ChatGPT, offer the advantage of delivering more precise answers. Studies have shown that LLMs can comprehend codes, assist in system architectural design, and …
Characterising Reproducibility Debt In Scientific Software: A Systematic Literature Review,
2025
Singapore Management University
Characterising Reproducibility Debt In Scientific Software: A Systematic Literature Review, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin
Research Collection School Of Computing and Information Systems
Context: In scientific software, the inability to reproduce results is often due to technical issues and challenges in recreating the full computational workflow from the original analysis. We conceptualise this problem as Reproducibility Debt (RpD). Much research has been performed to propose solutions to tackle these issues across various computational science disciplines. It is essential to identify and accumulate existing knowledge on reproducibility issues and state-of-the-art solutions so as to provide researchers and practitioners with information that enables further research activities and RpD management in practice. Objective: In the context of scientific software, we aim to characterise RpD by providing …
Verification Of Bit-Flip Attacks Against Quantized Neural Networks,
2025
Singapore Management University
Verification Of Bit-Flip Attacks Against Quantized Neural Networks, Yedi Zhang, Lei Huang, Pengfei Gao, Fu Song, Jun Sun, Jin Song Dong
Research Collection School Of Computing and Information Systems
In the rapidly evolving landscape of neural network security, the resilience of neural networks against bit-flip attacks (i.e., an attacker maliciously flips an extremely small amount of bits within its parameter storage memory system to induce harmful behavior), has emerged as a relevant area of research. Existing studies suggest that quantization may serve as a viable defense against such attacks. Recognizing the documented susceptibility of real-valued neural networks to such attacks and the comparative robustness of quantized neural networks (QNNs), in this work, we introduce BFAVerifier, the first verification framework designed to formally verify the absence of bit-flip attacks against …
Verifying Timed Properties Of Programs In Iot Nodes Using Parametric Time Petri Nets,
2025
Singapore Management University
Verifying Timed Properties Of Programs In Iot Nodes Using Parametric Time Petri Nets, Étienne André, Jean-Luc Béchennec, Sudipta Chattopadhyay, Sebastien Faucou, Didier Lime, Dylan Marinho, Olivier H. Roux, Jun Sun
Research Collection School Of Computing and Information Systems
The analysis of timed properties of programs is a complex task, as it is highly dependent on both the software and the hardware. In this work, we propose a framework for modeling with timed formal models the execution of programs, taking into account the micro-architecture of the machine on which it executes. We model both the program, at the instruction set architecture level, and the hardware, including the processor micro-architecture, using time Petri nets. Our implementation uses the ARM Cortex-M instruction set architecture and a hardware architecture representative of microcontrollers used in IoT nodes. The whole translation is fully automated …
How Developers Interact With Ai: A Taxonomy Of Human-Ai Collaboration In Software Engineering,
2025
Singapore Management University
How Developers Interact With Ai: A Taxonomy Of Human-Ai Collaboration In Software Engineering, Christoph Treude, Marco A. Gerosa
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code …
One-For-All: Towards Universal Domain Translation With A Single Stylegan,
2025
Singapore Management University
One-For-All: Towards Universal Domain Translation With A Single Stylegan, Yong Du, Jiahui Zhan, Xinzhe Li, Junyu Dong, Sheng Chen, Ming-Hsuan Yang, Shengfeng He
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space. To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P …
Dps: Design Pattern Summarisation Using Code Features,
2025
Singapore Management University
Dps: Design Pattern Summarisation Using Code Features, Najam Nazar, Sameer Sikka, Christoph Treude
Research Collection School Of Computing and Information Systems
Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques -- and more recently, large language models -- have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored.Our approach utilises code features and JavaParser to parse the code and create a JSON representation. …
A Functional Software Reference Architecture For Llm-Integrated Systems,
2025
Singapore Management University
A Functional Software Reference Architecture For Llm-Integrated Systems, Alessio Bucaioni, Martin Weyssow, Junda He, Yunbo Lyu, David Lo
Research Collection School Of Computing and Information Systems
The integration of large language models into software systems is transforming capabilities such as natural language understanding, decision-making, and autonomous task execution. However, the absence of a commonly accepted software reference architecture hinders systematic reasoning about their design and quality attributes. This gap makes it challenging to address critical concerns like privacy, security, modularity, and interoperability, which are increasingly important as these systems grow in complexity and societal impact. In this paper, we describe our emerging results for a preliminary functional reference architecture as a conceptual framework to address these challenges and guide the design, evaluation, and evolution of large …
Scuzer: A Scheduling Optimization Fuzzer For Tvm,
2025
Singapore Management University
Scuzer: A Scheduling Optimization Fuzzer For Tvm, Xiangxiang Chen, Xingwei Lin, Jingyi Wang, Jun Sun, Jiashui Wang, Wenhai Wang
Research Collection School Of Computing and Information Systems
The concept of Deep Learning (DL) compiler was proposed to deploy DL models more efficiently on diverse hardware through optimization techniques. As one of the most popular DL compilers, TVM incorporates three levels (high-level, schedule, and low-level) of optimizations, which can inadvertently introduce code logic bugs and build failure bugs. Among these optimizations, scheduling optimization is the core component of DL compilers, which ensures the acceleration of models on all devices. However, the existing works only focus on the testing of high-level and low-level optimizations in TVM, fail to take the most important and challenging intermediate scheduling optimization layer into …
Ada-Gen: Iterative And Incremental Generation Of Full-Stack Apps For Learning Agile/Devops Software Development Practices,
2025
Singapore Management University
Ada-Gen: Iterative And Incremental Generation Of Full-Stack Apps For Learning Agile/Devops Software Development Practices, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
To learn Agile/DevOps practices effectively, students need to apply them in an actual software development project. This is challenging if students are mostly from non-computing backgrounds and they do not have time in the curriculum to learn programming and related tools. Therefore, it is important to help students who do not possess programming foundations to develop fully functional software during the process of learning Agile/DevOps concepts. We noted that existing low-code/no-code app development platforms have not been designed to teach Agile/DevOps practices. On the other hand, recent AI-based tools for code generation such as GitHub Copilot have been built mainly …
Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces,
2025
United Arab Emirates University
Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces, Zaid Shuqfa
Dissertations
Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), enable direct communication between the brain and external devices without the involvement of peripheral nerves or muscles. Among various BCI paradigms, motor imagery (MI)–based BCIs are particularly appealing due to their intuitive, cue-independent nature, allowing users to issue control commands at will. MI–BCIs hold substantial promise for improving the quality of life of individuals with motor impairments, as well as enhancing hands-free control for healthy users. However, their widespread adoption remains limited by challenges such as low signal-to-noise ratio, inter- and intra-subject variability, and the need for frequent calibration. These challenges …
Code Of Faith: Programming Spirituality In The Digital Age,
2025
Liberty University
Code Of Faith: Programming Spirituality In The Digital Age, Caleb Martin
Senior Honors Theses
With the increasing pervasiveness of technology in our daily lives, it is critical to consider the potential effects on an individual's religious practices and beliefs from software applications developed within a religious framework. This thesis delves into the relationship between software development and religious experiences, examining the manner in which the creation, operation, and application of religious technology can mold and impact an individual's spiritual development. This thesis aims to inform the implementation of technologies with a nonsecular application, ensuring that they are developed with a deep respect for the nuances of religious experience. The findings of this study will …
A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics,
2025
United Arab Emirates University
A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor
Theses
The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a datadriven recommendation framework that predicts Course Learning Outcome (CLO) achievement scores using regression, and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics. This study analyzed 100 undergraduate and postgraduate courses from the College of Information …
Cachealarm: Monitoring Sensitive Behaviors Of Android Apps Using Cache Side Channel,
2025
Singapore Management University
Cachealarm: Monitoring Sensitive Behaviors Of Android Apps Using Cache Side Channel, Jianwen Tian, Haoyu Ma, Debin Gao, Xiaohui Kuang
Research Collection School Of Computing and Information Systems
Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android’s own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this …
Adapting Knowledge Prompt Tuning For Enhanced Automated Program Repair,
2025
Singapore Management University
Adapting Knowledge Prompt Tuning For Enhanced Automated Program Repair, Xuemeng Cai, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Automated Program Repair (APR) aims to enhance software reliability by automatically generating bug-fixing patches. Recent work has improved the state-of-the-art of APR by fine-tuning pre-trained large language models (LLMs), such as CodeT5, for APR. However, the effectiveness of fine-tuning be-comes weakened in data scarcity scenarios, and data scarcity can be a common issue in practice, limiting fine-tuning performance. To alleviate this limitation, this paper adapts prompt tuning for enhanced APR and conducts a comprehensive study to evaluate its effectiveness in data scarcity scenarios, using three LLMs of different sizes and six diverse datasets across four programming languages. Prompt tuning rewrites …
Adaptive Deviation Learning For Visual Anomaly Detection With Data Contamination,
2025
Singapore Management University
Adaptive Deviation Learning For Visual Anomaly Detection With Data Contamination, Aanindya Sundar Das, Guansong Pang, Monowar Bhuyan
Research Collection School Of Computing and Information Systems
Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal samples, assuming an absence of contamination; a condition often unmet in real-world scenarios. The performance of these methods significantly depends on the quality of the data and usually decreases when exposed to noise. We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end while addressing data contamination by assigning relative importance to the …
