Singapore Management University

Optimizing Sequential Competitive Facility Location and Attractiveness Design

We consider a sequential competitive facility location and attractiveness design problem (S-CFLAP) in which the leader and follower sequentially decide on the location and attractiveness design (e.g., building area) of new facilities to maximize their market shares. Customer dema...

ChatGPT, Can You Generate Multiple-Choice Questions for an Undergraduate Computing Course? An Evaluation of its Effectiveness Using Classical Test Theory

Educators are increasingly adopting large language models such as ChatGPT to generate multiple-choice questions (MCQs), yet empirical evidence on the quality of these items in computing education assessments remains limited. This study compares 30 ChatGPT-generated and 30 manuall...

Exploring rehabilitation therapists’ knowledge and perspectives on the use of artificial intelligence and machine learning for persons post-stroke

Background The integration of artificial intelligence (AI) and machine learning (ML) within the clinical work of rehabilitation therapists in post stroke rehabilitation has the potential to transform their work performance, habits, and routines. The impact could potentially enhan...

Strength in families: Parental resilience, marital dynamics, and adolescent anxiety and substance use

Abstract Drawing on Luthar’s relational framework of resilience, this study examines how parental resilience operates within marital dynamics to shape adolescent adjustment in Mexican-origin families. Using four waves of data from 604 families (M adolage = 12.41, SD = 0.97; 54.3%...

Leading AI-Driven Digital Transformation: A Review of Leadership Attributes and Practices, with Implications for Singapore

The rapid evolution of artificial intelligence (AI) is reshaping how enterprises are led, organised, and governed, raising critical questions about which leadership attributes and practices best enable AI-driven digital transformation (AIDT). Enterprises face growing pressure to...

Agentic Software Issue Resolution with Large Language Models: A Survey

Software issue resolution task aims to address real-world issues in software repositories based on natural language descriptions provided by users, representing a key aspect of software maintenance. With the rapid development of large language models (LLMs) in reasoning and gener...

To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair

LLM-based agents for program repair are increasingly built on a “generate-run-revise” paradigm, iteratively executing tests to evaluate and refine patches. This execution-based approach has become standard practice in state-of-the-art systems. However, executions can be time-cons...

Awe as a Transformative Emotion: An Integrative Review, Metamorphosis Model, and Research Agenda

Awe has attracted substantial scholarly attention but its implications for management remains fragmented across different accounts of what awe is and why it matters. In this review, we first show that the literature has developed around three dominant perspectives—collective self...

DDOR: Delta Debugging for Explainable Overrefusal Testing and Repair

While safety alignment and guardrails help large language models (LLMs) avoid harmful outputs, they can also induce overrefusal, i.e., unwarranted rejection of benign queries that merely appear risky. We present DDOR (Delta Debugging for OverRefusal), a fully automated and explai...

Mutation-based multi-agent test case update

Modern software systems evolve rapidly under CI/CD practices, where tests are critical for quality. However, substantial code changes often render existing test cases obsolete, causing pipeline disruptions, reduced productivity, and compromised quality. Recent automatic test upda...

An Empirical Study of Speculative Decoding on Software Engineering Tasks

Large Language Models (LLMs) have become widely used for Software Engineering (SE) tasks, spanning from function-level code generation to complex repository-level workflows. However, the high latency of autoregressive inference remains a significant bottleneck, hindering their de...

On the Evaluation of Large Language Models in Unit Test Evolution (Experience Paper)

Large language models (LLMs) have recently shown promising potential in automating unit test evolution for evolving software systems. However, the effectiveness of LLMs in unit test evolution remains insufficiently understood, particularly with respect to prompt design choices, i...

Code–Test Co-translation: Towards Practical and Effective Program Migration in the Wild

Program migration, which involves translating software systems from one programming language to another, is essential for modernizing legacy systems and improving maintainability. Recent large language models (LLMs) have demonstrated strong performance in code translation; howeve...

Seeing Is Coding: On the Effectiveness of Vision Language Models in Code Understanding

Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear...

Learning from the Test: Self-Referential Differential Testing for Deep RL Agents

Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in real-world applications, ensuring their quality and reliability is paramount. Current works primarily focus on detecting safety-crit...

MuMuTestUp: Mutation-Based Multi-agent Test Case Update

Modern software systems evolve rapidly under continuous integration and deployment (CI/CD) practices, in which tests act as critical gatekeepers of software quality. However, when substantial code changes are introduced, existing test cases may become obsolete, leading to compila...

Understanding and Improving Model Editing for Secure Code Generation

Large language models (LLMs) are widely used for code generation but may reproduce vulnerabilities learned from insecure training data. Prior work mainly relies on inference-time hardening, which introduces auxiliary components and runtime overhead. We present the first systemati...

CRAFT: Exploring Wearable Creative AI on Smart Glasses for Fiction Writing in Real-World Contexts

Creative writing increasingly integrates AI assistance, yet current tools miss in-situ moments when writers draw inspiration from real-world experiences. We envision Context-aware Reality-Fiction Transformation ( CRAFT ), an approach for AI glasses that translates daily experienc...

SEZ-HARN: Self-explainable Zero-shot Human Activity Recognition Network

Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) data has numerous applications in healthcare and assisted living. However, its deployment in real-world scenarios is constrained by the limited availability of labeled datasets covering diverse activities and...

Extrapolating CSR–HRM interdependencies and employee well-being for sustainable workplaces in generative AI era

The development of generative AI (GAI) capabilities has led corporate entities to rethink their strategic initiatives towards sustainable workforce management. This dynamism has been broadly identified as a concern for managerial teams. Despite strong consensus on the roles of or...