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Articles 961 - 990 of 13123
Full-Text Articles in Entire DC Network
Acquisition Strategy For Innovation Under Competence-Destroying Change: Empirical Study—Pharmaceutical Company's Biotechnology Company Acquisition, Joo Tae Sung
Dissertations and Theses Collection (Open Access)
In response to declining R&D productivity and successive patent cliffs, pharmaceutical companies are increasingly acquiring biotechnology firms to sustain innovation. These acquisitions often occur under conditions of competence-destroying change, where the acquirer's chemistry-based knowledge base does not overlap with the biotechnology expertise of the target, challenging traditional theories that emphasize absorptive capacity and knowledge similarity.
This study introduces therapeutic knowledge, defined as the acquirer’s expertise in specific disease areas, as a distinct and critical dimension that interacts with both chemistry-based and biotechnology-based knowledge. Using a dataset of 212 observations across therapeutic areas from 80 acquisitions, the analysis uncovers an inverted …
Growing Through Grit And Grace, Roshni Mahtani Cheung
Growing Through Grit And Grace, Roshni Mahtani Cheung
Asian Management Insights
An interview with Roshni Mahtani Cheung on how she scaled up theAsianparent and The Parentinc with resilience and a ‘community-first’ approach. How Roshni Mahtani Cheung scaled up theAsianparent and The Parentinc with resilience and a ‘community-first’ approach.
Driving Financial Inclusion Through Central Bank Digital Currencies: A Methodology For Cbdc Implementation, Heng Wang
Research Collection Yong Pung How School Of Law
This publication outlines a methodology for the design, testing, and implementation of central bank digital currencies (CBDCs) to advance financial inclusion. As an increasing number of central banks around the world explore this novel form of financial infrastructure, this paper focuses on the potential of CBDCs to address key access and price barriers in developing and emerging economies. CBDCs, especially retail ones, can offer unique opportunities for enhancing financial services accessibility, affordability, and usability for unserved and underserved populations, if implemented responsibly through a structured, inclusive and user-centric approach.
Ecological Responsibility In The City: The Surfaces And Volumes Of Urban Mosquito Management, Orlando Woods, Sayd Randle
Ecological Responsibility In The City: The Surfaces And Volumes Of Urban Mosquito Management, Orlando Woods, Sayd Randle
Research Collection College of Integrative Studies
This paper explores how ecological responsibility becomes distributed across the surfaces and volumes of the city. Our focus is urban mosquito management – specifically, the management of the Aedes aegypti mosquito, the vector by which dengue and several other diseases are transmitted to humans – and the governance strategies deployed in Singapore to manage breeding sites and mosquito outbreaks. Whilst the governance of nature in Singapore is usually undertaken by the state, the effective management of mosquitoes requires responsibility for ecological management to become distributed throughout the city. Residents are expected to assume responsibility for surficial cleanliness and the removal …
Infrastructural Splintering Along The Bri: Catholic Political Ecologies And The Fractious Futures Of Sri Lanka’S Littoral Spaces, Orlando Woods
Infrastructural Splintering Along The Bri: Catholic Political Ecologies And The Fractious Futures Of Sri Lanka’S Littoral Spaces, Orlando Woods
Research Collection College of Integrative Studies
This article considers the ways in which the material infrastructures of China’s Belt and Road Initiative (BRI) intersect with other infrastructural formations, and how the resulting overlaps can trigger processes of what I call “infrastructural splintering”. These processes cause infrastructure to be experienced in differentiating ways, creating divisive politics where there might once have been unity. Embracing these politics as an analytical starting point undermines the techno-material stability of the BRI, and reveals its more-than-material affects. I illustrate these ideas by developing a case study of the effects of the China-backed Colombo Port City project on Catholic fishing communities that …
A Summoned Life: Vocation In The Analects, Teng-Kuan Ng
A Summoned Life: Vocation In The Analects, Teng-Kuan Ng
Research Collection College of Integrative Studies
This article explores the concept of vocation in the Analects. While most treatmentsof vocation focus on the Christian tradition, and while Confucian thought is oftenviewed as categorically nontheistic, Robert Merrihew Adams’s Finite and InfiniteGoods (1999) presents an illuminating comparative framework for tracing outKongzi’s religious ethical vision of how persons think, act, and feel when graspedby a sense of divine calling. I argue that in the Analects a vocation is compositelyconceived as: (1) a special divine imperative to an individual or a community (2) tolove particular goods within one’s actual circumstances (3) with nonconsequentialist,noncompetitive, and potentially counter-cultural commitment (4) such that …
Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong
Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Background: Increasingly, students are using ChatGPT to assist them in learning and even completing their assessments, raising concerns of academic integrity and loss of critical thinking skills. Many articles suggested educators to redesign assessments which are more “Generative-AI-resistant” and to focus on assessing students on higher order thinking skills. However, there is a lack of articles that attempt to quantify assessments at different cognitive levels to provide empirical study insights on ChatGPT’s performance at different levels, which will affect how educators redesign their assessments.Objectives: Educators need new information on how well ChatGPT performs to redesign future assessments to assess their …
On-Demand Scenario Generation For Testing Automated Driving Systems, Songyang Yan, Xiaodong Zhang, Kunkun Hao, Haojie Xin, Yonggang Luo, Jucheng Yang, Ming Fan, Chao Yang, Jun Sun, Zijiang Yang
On-Demand Scenario Generation For Testing Automated Driving Systems, Songyang Yan, Xiaodong Zhang, Kunkun Hao, Haojie Xin, Yonggang Luo, Jucheng Yang, Ming Fan, Chao Yang, Jun Sun, Zijiang Yang
Research Collection School Of Computing and Information Systems
The safety and reliability of Automated Driving Systems (ADS) are paramount, necessitating rigorous testing methodologies to uncover potential failures before deployment. Traditional testing approaches often prioritize either natural scenario sampling or safety-critical scenario generation, resulting in overly simplistic or unrealistic hazardous tests. In practice, the demand for natural scenarios (e.g., when evaluating the ADS's reliability in real-world conditions), critical scenarios (e.g., when evaluating safety in critical situations), or somewhere in between (e.g., when testing the ADS in regions with less civilized drivers) varies depending on the testing objectives. To address this issue, we propose the On-demand Scenario Generation (OSG) Framework, …
Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau
Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau
Research Collection School Of Computing and Information Systems
With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works …
Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu
Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu
Research Collection School Of Computing and Information Systems
Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …
Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning
Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning
Research Collection School Of Computing and Information Systems
Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that …
Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo
Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo
Research Collection School Of Computing and Information Systems
The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading …
Collaborative Tree Search For Enhancing Embodied Multi-Agent Collaboration, Lizheng Zu, Lin Lin, Song Fu, Na Zhao, Pan Zhou
Collaborative Tree Search For Enhancing Embodied Multi-Agent Collaboration, Lizheng Zu, Lin Lin, Song Fu, Na Zhao, Pan Zhou
Research Collection School Of Computing and Information Systems
Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task completion. Previous approaches with simple communication patterns carry erroneous or incoherent agent actions, which can lead to additional risks. To address these problems, we propose Cooperative Tree Search (CoTS), a framework designed to significantly improve collaborative planning and task execution efficiency among embodied agents. CoTS guides multi-agents to discuss long-term strategic plans within a modified Monte Carlo tree, searching along LLMdriven reward functions to provide a more thoughtful and promising approach to …
Hvi: A New Color Space For Low-Light Image Enhancement, Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang
Hvi: A New Color Space For Low-Light Image Enhancement, Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang
Research Collection School Of Computing and Information Systems
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable inten sity. The former enforces …
Why Does My Transaction Fail? A First Look At Failed Transactions On The Solana Blockchain, Xiaoye Zheng, Zhiyuan Wan, David Lo, Difan Xie, Xiaohu Yang
Why Does My Transaction Fail? A First Look At Failed Transactions On The Solana Blockchain, Xiaoye Zheng, Zhiyuan Wan, David Lo, Difan Xie, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting …
Large Language Models For Logical Fallacy Detection, Nicole Anne Hui-Ying Teo, Donghao Huang, Erik Cambria, Zhaoxia Wang
Large Language Models For Logical Fallacy Detection, Nicole Anne Hui-Ying Teo, Donghao Huang, Erik Cambria, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Identifying logical fallacies is essential for maintaining log-ical reasoning and reducing false information in a variety of domains, such as the media, law, and education. We present an extensive study on the use of large language models (LLMs) for logical fallacy detection and provide a comparative overview of model performance across various fallacy classes. We evaluate the logical fallacy detection capabilities of multiple state-of-the-art models (LLaMA, Qwen, Gemma, Phi) utilizing accuracy, precision, recall, and F1-score as assessment measures. Accord-ing to our findings, our models do well on simple fallacies like “circular reasoning,” but they have trouble with more interpretive reasoning …
Potential And Pitfalls Of Romantic Artificial Intelligence (Ai) Companions: A Systematic Review, Qi Hui Jerlyn Ho, Meilan Hu, Tracy Xi Chen, Andree Hartanto
Potential And Pitfalls Of Romantic Artificial Intelligence (Ai) Companions: A Systematic Review, Qi Hui Jerlyn Ho, Meilan Hu, Tracy Xi Chen, Andree Hartanto
Research Collection School of Social Sciences
As Artificial Intelligence (AI) becomes more integrated into daily life, individuals have increasingly turned to AIdriven systems for emotional support, companionship, and even romantic relationships. These relationships can be both beneficial and detrimental. Given the need for a comprehensive understanding of this phenomenon, this systematic review uses Sternberg’s Triangular Theory of Love to provide a holistic summary of its key potentials and pitfalls. A total of 23 articles were identified from the following databases: EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus, and Web of Science. Results highlighted the key potentials of being in a romantic relationship with AI companions as: the …
Stress Reactivity And Sociocultural Learning: More Stress-Reactive Individuals Are Quicker At Learning Sociocultural Norms From Experiential Feedback, Shilpa Madan, Krishna Savani, Pranjal H. Mehta, Desiree Y. Phua, Ying-Yi Hong, Michael W. Morris
Stress Reactivity And Sociocultural Learning: More Stress-Reactive Individuals Are Quicker At Learning Sociocultural Norms From Experiential Feedback, Shilpa Madan, Krishna Savani, Pranjal H. Mehta, Desiree Y. Phua, Ying-Yi Hong, Michael W. Morris
Research Collection Lee Kong Chian School Of Business
When interacting with others in unfamiliar sociocultural settings, people need to learn the norms guiding appropriate behavior. The present research investigates an individual difference that helps this kind of learning: stress reactivity. Interactions in an unfamiliar sociocultural setting are stressful, particularly when the actor fails to follow its rules. Although stress is typically considered a liability, more stress-reactive individuals may be more motivated to improve, and, thus, quicker to learn these rules. Consistent with this idea, a pilot study found that people genetically inclined to stress reactivity, as computed by a genetic profile score across 59 single nucleotide polymorphisms on …
Capacity Investment In The Presence Of Correlated Demand And Production Resource Uncertainties And Its Implications For Financial Hedging, Onur Boyabatli, Guiyun Feng
Capacity Investment In The Presence Of Correlated Demand And Production Resource Uncertainties And Its Implications For Financial Hedging, Onur Boyabatli, Guiyun Feng
Research Collection Lee Kong Chian School Of Business
In manufacturing firms, besides demand uncertainty, capacity investment decisions may also be subject to uncertainty in the availability of a production resource (e.g., budget or commodity component) which may become constraining for manufacturing. When the production resource uncertainty is tied to a financial index (e.g., asset or commodity price), the firm can enter into financial hedging contracts at the time of capacity investment to engineer this uncertainty. Our paper characterizes the optimal capacity investment and hedging decisions and examines how production resource uncertainty impacts the firm’s decisions, profitability, and the value of hedging. We identify three key drivers of these …
Hardiness, Optimism And Hope: Longitudinal Study Of How Individuals May Build Resilience Through Experiential Undergraduate University Courses, Paul Lim, Chin Heng Low, Kevin Koh
Hardiness, Optimism And Hope: Longitudinal Study Of How Individuals May Build Resilience Through Experiential Undergraduate University Courses, Paul Lim, Chin Heng Low, Kevin Koh
Research Collection Lee Kong Chian School Of Business
This study explores the antecedents of students’ resilience in a management education setting, by looking at the role of state hope and dispositional optimism in cultivating their psychological hardiness. While resilience is recognized as integral to personal growth, extant research has predominantly focused on its correlations with psychological capital (PsyCap) elements such as hope and optimism; or examined the outcomes of hardiness as a proxy of resilience. Little has been done to explicate the directional causal relationships among the PsyCap elements and hardiness in view of resilience-building. Using a longitudinal mixed methods design, this study delves into how state hope, …
On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic
On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic
Research Collection School Of Computing and Information Systems
We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some …
Building Narratives And Probing Concepts: Preparing Materials For Co-Design With Autistic Livestreamers, Terrance Mok, Tyson Hartley, Anthony Tang, Adam Mccrimmon, Lora Oehlberg
Building Narratives And Probing Concepts: Preparing Materials For Co-Design With Autistic Livestreamers, Terrance Mok, Tyson Hartley, Anthony Tang, Adam Mccrimmon, Lora Oehlberg
Research Collection School Of Computing and Information Systems
Based on ten semi-structured interviews with autistic Twitch streamers, we introduce a series of scenario-based design narratives coupled with technology design concepts as a starting point for co-design discussion about autistic streaming. This work builds on prior thematic analysis of the unique intersection between autism and livestreaming. Our user-centered scenarios highlight the needs, goals, and challenges of autistic individuals in livestreaming contexts. By using evocative narratives, the scenarios serve to facilitate empathy and deeper engagement with the needs of autistic users, and help facilitate and support co-creative dialogues and discussions about new technology designs. We contribute this starting point for …
Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun
Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Training large foundation models of remote-sensing (RS) images is almost impossible due to the limited and long-tailed data problems. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module …
On-Demand Heterogeneous Drone Delivery Problem, Xupeng Wen, Zhiguang Cao, Shu Xu, Dapeng Ren, Guohua Wu, Yaoxin Wu
On-Demand Heterogeneous Drone Delivery Problem, Xupeng Wen, Zhiguang Cao, Shu Xu, Dapeng Ren, Guohua Wu, Yaoxin Wu
Research Collection School Of Computing and Information Systems
In the on-demand problem domain, actual demand frequently deviates from the expected demand. This paper intricately delves into the exploration of on-demand heterogeneous multi-drone routing problem (ODHDRP), in which a transport drone carries multiple terminal drones to subregions in the first echelon, and the terminal drones deliver parcels during a flight trip to customers with demands in subregions to maintain economies of scale in the second echelon. We formulate the customer demands using a normal distribution, and exploit a reliability model of customer demands with chance constraints. To solve the ODHDRP efficiently, we propose a hybrid iterative optimisation heuristic (HIOH) …
Outperforming The Best With Minimal Effort: Algorithm Selection For Constrained Multi-Objective Optimization, Mustafa Misir, Aldy Gunawan
Outperforming The Best With Minimal Effort: Algorithm Selection For Constrained Multi-Objective Optimization, Mustafa Misir, Aldy Gunawan
Research Collection School Of Computing and Information Systems
The present study performs algorithm selection on a suite of optimization algorithms targeting the constrained multi-objective optimization problems. The idea is to utilize the existing, relevant algorithmic experience in the literature to deliver an improved solver with limited effort. The reason being that algorithm development, in general, is a challenging and time-consuming process, especially with the goal of outperforming the existing methods from varying perspectives such as performance, speed, and robustness. Concerning the multi-objective optimization problems, the required development efforts happen to be even harder than addressing the single-objective ones. Furthermore, referring to the fact that the number of candidate …
Alayadb: The Data Foundation For Efficient And Effective Long-Context Llm Inference, Yangshen Deng, Zhengxin You, Long Xiang, Qilong Li, Peiqi Yuan, Zhaoyang Hong, Yitao Zheng, Wanting Li, Runzhong Li, Haotian Liu, Kyriakos Mouratidis, Man Lung Yiu, Huan Li, Qiaomu Shen, Rui Mao, Bo Tang
Alayadb: The Data Foundation For Efficient And Effective Long-Context Llm Inference, Yangshen Deng, Zhengxin You, Long Xiang, Qilong Li, Peiqi Yuan, Zhaoyang Hong, Yitao Zheng, Wanting Li, Runzhong Li, Haotian Liu, Kyriakos Mouratidis, Man Lung Yiu, Huan Li, Qiaomu Shen, Rui Mao, Bo Tang
Research Collection School Of Computing and Information Systems
AlayaDB is a cutting-edge vector database system natively architected for efficient and effective long-context inference for Large Language Models (LLMs) at AlayaDB AI. Specifically, it decouples the KV cache and attention computation from the LLM inference systems, and encapsulates them into a novel vector database system. For the Model as a Service providers (MaaS), AlayaDB consumes fewer hardware resources and offers higher generation quality for various workloads with different kinds of Service Level Objectives (SLOs), when compared with the existing alternative solutions (e.g., KV cache disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that it abstracts the attention computation …
Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao
Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao
Research Collection School Of Computing and Information Systems
Recent advancements in large language models (LLMs) have significantly improved code generation, which generates code snippets automatically based on natural language requirements. Despite achieving state-of-the-art performance, LLMs often struggle to generate accurate and reliable code, requiring developers to spend substantial effort debugging and evaluating the generated output. Researchers have proposed leveraging Consistency to select code that passes more tests (inter-consistency) and demonstrates consistent behavior across more counterparts (intra-consistency). However, since the tests themselves are also generated by LLMs, relying on majority voting based on incorrect tests leads to unreliable results. To address this, we propose a lightweight interaction framework that …
Enhancing Vulnerability Detection Via Inter-Procedural Semantic Completion, Bozhi Wu, Chengjie Liu, Zhiming Li, Yushi Cao, Jun Sun, Shang-Wei Lin
Enhancing Vulnerability Detection Via Inter-Procedural Semantic Completion, Bozhi Wu, Chengjie Liu, Zhiming Li, Yushi Cao, Jun Sun, Shang-Wei Lin
Research Collection School Of Computing and Information Systems
Inspired by advances in deep learning, numerous learning-based approaches for vulnerability detection have emerged, primarily operating at the function level for scalability. However, this design choice has a critical limitation: many vulnerabilities span multiple functions, causing function-level approaches to lose the semantics of called functions and fail to capture true vulnerability patterns. To address this issue, we propose VulnSC, a novel framework designed to enhance learning-based approaches by complementing inter-procedural semantics. VulnSC retrieves the source code of called functions for datasets and leverages large language models (LLMs) with well-designed prompts to generate summaries for these functions. The datasets, enhanced with …
Reaccept: Automated Co-Evolution Of Production And Test Code Based On Dynamic Validation And Large Language Models, Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu, Di Cui, Jianguo Sun, Jun Sun
Reaccept: Automated Co-Evolution Of Production And Test Code Based On Dynamic Validation And Large Language Models, Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu, Di Cui, Jianguo Sun, Jun Sun
Research Collection School Of Computing and Information Systems
Synchronizing production and test code, known as PT co-evolution, is critical for software quality. Given the significant manual effort involved, researchers have tried automating PT co-evolution using predefined heuristics and machine learning models. However, existing solutions are still incomplete. Most approaches only detect and flag obsolete test cases, leaving developers to manually update them. Meanwhile, existing solutions may suffer from low accuracy, especially when applied to real-world software projects. In this paper, we propose ReAccept, a novel approach leveraging large language models (LLMs), retrievalaugmented generation (RAG), and dynamic validation to fully automate PT co-evolution with high accuracy. ReAccept employs an …
De-Duplicating Silent Compiler Bugs Via Deep Semantic Representation, Junjie Chen, Xingyu Fan, Chen Yang, Shuang Liu, Jun Sun
De-Duplicating Silent Compiler Bugs Via Deep Semantic Representation, Junjie Chen, Xingyu Fan, Chen Yang, Shuang Liu, Jun Sun
Research Collection School Of Computing and Information Systems
The compiler bug duplication problem (where many test failures are caused by the same compiler bug) can lead to huge waste of time and resource in diagnosing test failures produced by compiler testing. It is particularly challenging with regard to the silent compiler bugs that do not produce any error messages. To address this problem, multiple white-box techniques were proposed, but they are inapplicable in many practical scenarios. Black-box techniques are more practical, but the existing ones are less effective as they often rely on irrelevant syntactic information. To bridge this gap, we propose a novel black-box technique (BLADE), which …