25 papers · ranked by Valyu relevance
Xueyu Hu, Kun Kuang, Jiankai Sun, Hongxia Yang + 1 more
Large language models (LLMs) have made significant progress in code generation tasks, but their performance in tackling programming problems with complex data structures and algorithms remains suboptimal. To address this issue, we propose an in-context learning approach that guides LLMs to debug by using a "print…
Wang Jing, Liu Shang, Lu Yao, Xie + 1 more
—High-level synthesis (HLS) accelerates hardware design by enabling the automatic translation of high-level descriptions into efficient hardware implementations. However, debugging HLS code is a challenging and labor-intensive task, especially for novice circuit designers or software engineers without sufficient…
Huifang Ma, Zhicheng Ji
Large language models have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges remains poorly understood. We conducted a classroom experiment in which graduate students used large language models (LLMs) to solve biomedical data science challenges on Kaggle.…
Elena Maria Tosca, Ludovica Aiello, Alessandro De Carlo, Paolo Magni + 1 more
Background: Large Language Models (LLMs) have driven significant advances in artificial intelligence (AI), with transformative applications across numerous scientific fields, including biomedical research and drug development. However, despite growing interest in adjacent domains, their adoption in pharmacometrics, a…
Petr Ročkai, Jǐŕı Barnat
In this paper, we introduce an interactive simulator for programs in the form of LLVM bitcode. The main features of the simulator include precise control over thread scheduling, automatic checkpoints and reverse stepping, support for source-level information about functions and variables in C and C++ programs and…
Giuseppe Antonio Di Luna, Davide Italiano, Luca Massarelli, Sebastian Österlund + 2 more
'Sebastian Österlund' 'Cristiano Giuffrida' 'Leonardo Querzoni'] Despite the advancements in software testing, bugs still plague deployed software and result in crashes in production. When debugging issues —sometimes caused by "heisenbugs"— there is the need to interpret core dumps and reproduce the issue offline on…
Jacob Kreindl, Manuel Rigger, Hanspeter Mössenböck
Many dynamic programming languages such as Ruby and Python enable developers to use so called native extensions, code implemented in typically statically compiled languages like C and C++. However, debuggers for these dynamic languages usually lack support for also debugging these native extensions. GraalVM can execute…
Anthony Savidis, Vangelis Tsiatsianas
Debugging is an essential process with a large share of the development effort, being a relentless quest for offensive code through tracing, inspection and iterative running sessions. Probably every developer has been in a situation with a clear wish to rewind time just for a while, only to retry some actions…
Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Mark Galea + 7 more
Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack…
Paul Leger, Felipe Ruiz, Hiroaki Fukuda, Nicolás Cardozo + 1 more
'Muhammad Aleem'] JavaScript Web applications are a common product in industry. As with most applications, Web applications can acquire software flaws (known as bugs), whose symptoms are seen during the development stage and, even worse, in production. The use of debuggers is beneficial for detecting bugs.…
Robert H. Tai, Lillian R. Bentley, Xin Xia, Jason M. Sitt + 3 more
The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample…
Jacqueline A Jansen, Artür Manukyan, Nour Al Khoury, Altuna Akalin
Data analysis is constrained by a shortage of skilled experts, particularly in biology, where detailed data interpretation is vital for understanding complex biological processes and developing new treatments and diagnostics. To address this, we developed mergen, an R package that leverages Large Language Models (LLMs)…
Geonbae Na, Jongsu Lim, Sunjun Lee, Jeong Hyun Yi
As interest in Internet of Things environments rapidly increases throughout the IT convergence field, compatibility with mobile devices must be provided to enable personalized services. The security of mobile platforms and applications is critical because security vulnerabilities of mobile devices can be spread to all…
Authors not listed
Utilizing Large Language Models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding hallucinations, and extending their scope to research topics outside their…
Yeongung Park, Seokwoo Choi, Un Yeong Choi, Haimin Jin + 2 more
'Nurul Harzira Mohamad Nor' 'Yongsu Park'] As IoT devices are being widely used, malicious code is increasingly appearing in Linux environments. Sophisticated Linux malware employs various evasive techniques to deter analysis. The embedded trace microcell (ETM) supported by modern Arm CPUs is a suitable hardware tracer…
Kevin Kawchak
Large Multimodal Models (LMMs) possess the ability to analyze chemical spectra of an organic compound using state of the art conversational AI. These outputs can then be chained together and introduced as a text input for other LLMs or LMMs to predict the compound name. Here, a challenging 15 carbon molecule problem…
Authors not listed
Traditional nanocrystal ligand design often relies on trial-and-error, limiting systematic exploration. Here, we present a framework integrating a Large Language Model (LLM) agent into experimental planning, iteratively refining ligand design for nanocrystal synthesis. Through structured human-AI dialogue, we generated…
Kent Milfeld, Bronis R. de Supinski, Lars Koesterke, Jannis Klinkenberg + 4 more
'Jannis Klinkenberg' 'Lechen Yu' 'Joachim Protze' 'Oscar Hernandez' 'Vivek Sarkar'] Incorrect usage of OpenMP constructs may cause different kinds of defects in OpenMP applications. Most of the existing work focuses on concurrency bugs such as data races and deadlocks, since concurrency bugs are difficult to detect and…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Debjyoti Bhattacharya, Harrison Cassady, Michael Hickner, Wesley Reinhart
The design of small molecules is crucial for technological applications ranging from drug discovery to energy storage. Due to the vast design space available to modern synthetic chemistry, the community has increasingly sought to use data-driven and machine learning approaches to navigate this space. Although…
Fernando Cruz, João Capela, Eugénio C. Ferreira, Miguel Rocha + 1 more
As the reconstruction of Genome-Scale Metabolic Models becomes standard practice in systems biology, the number of organisms having at least one metabolic model at the genome-scale is peaking at an unprecedented scale. The automation of several laborious tasks, such as gap-finding and gap-filling, allowed to develop…
Authors not listed
Generative artificial intelligence (AI) tools such as large language models (LLMs) have become ubiquitous in everyday life, and are also increasingly finding applications in the chemical sciences. Although LLMs have achieved impressive performance on many chemistry tasks, optimal performance requires proper use…
Authors not listed
Bioprocess engineering has incorporated effective AI applications in recent years that consist of traditional approaches to training models on relevant data to then analyze and predict new and unseen data. The missing component has been the ability to process mixed data from an assortment of dissimilar information…
Yuanqi Du, Chenru Duan, Andres Bran, Anna Sotnikova + 5 more
Large language models (LLMs) have demonstrated outstanding capabilities in general problem-solving and been shown to improve productivity in certain domains. Thanks to their flexibility, recent work has leveraged them for diverse scientific applications, ranging from predictive modeling, scientific Q&A, and even as…
John Whitington
Motivated by experience in programming and in the teaching of programming, we make another assault on the longstanding problem of debugging. Having explored why debuggers are not used as widely as one might expect, especially in functional programming environments, we define the characteristics of a debugger which make…