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3,118 papers
This paper formalizes test suite generation as a Markov Decision Process (MDP) and demonstrates that its objective exhibits monotone submodularity, which enables an effective relaxation of this NP-hard global optimization into a tractable step-wise greedy procedure.
Software testing is a critical, yet resource-intensive phase of the software development lifecycle. Search-based approaches typically achieve high coverage but produce tests with low readability, whereas large language model (LLM)-based methods generate more human-readable tests but often suffer from low coverage and c...
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, in-context learning (ICL) strategies, and...
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; however, existing methods and benchmarks still...
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Large Language Models (LLMs) have significantly improved programming efficiency by parsing natural language into code snippets. However, their performance degrades significantly as requirements scale; when faced with multi-modal documents containing hundreds of scenarios, LLMs often produce incorrect implementations or...
The HPC site profile is proposed, a structured, evidence-backed document that preflights a workflow into an execution plan or an early, explainable failure and is presented as a case study of preflighting a real workflow.
This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective, subfield-aw...
AuraForge is introduced to synthesize and validate executable security tests for training secure coding agents, and it is demonstrated that AuraForge provides more diverse and reliable security supervision to train secure coding agents.
Coding agents solve repository-level software engineering tasks through long trajectories of code inspection, search, editing, and testing. As a task progresses, earlier exploration becomes stale, so managing context is more than avoiding overflow: an agent must decide when to compact, what working state to preserve, a...
Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastructure. While learning-based schedulers excel in research, industrial deployments still rely on hand-written rules that operators can read, audit, and execute within tight...
As large language models (LLMs) keep growing in size and complexity, their training frameworks evolve at a rapid pace as well. Therefore, continuous integration (CI) is critical for maintaining the quality and stability of these frameworks. However, unlike traditional software, CI for LLM training frameworks relies on...
The purpose of this research is to examine the relationship between innovative behaviors and future visions of sports management students. The research was conducted within the framework of a relational survey model and was carried out using data obtained from 215 students (90 female, 125 male) studying in the Eastern...
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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