22 papers · ranked by Valyu relevance
Yihong Dong, Xue Jiang, Jiaru Qian, Wang Tian + 2 more
—Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm. Distinct from previous code generation techniques, code generation agents are characterized by three core features. 1) Autonomy: the ability to independently manage the entire workflow, from task…
Huy Le, Phong Nguyen, Hao Do, Tuan Nguyen + 3 more
Automatic code generation has long been regarded as a critical and aspirational goal in Software Engineering research [1, 2, [3]]. Its core objective is to translate userprovided requirements—often expressed in natural language—into executable source code, thereby streamlining the software development process. This…
Kehao Mao, Baokun Hu, Ruixin Lin, Zewen Li + 2 more
Automated programming has become a powerful tool for solving real-world problems. Code generation, in particular, plays a key role in improving developer productivity and reducing the entry barrier to software development. Recent advances in large language models (LLMs) have significantly improved program synthesis…
Man-Fai Wong, Shangxin Guo, Ching-Nam Hang, Siu-Wai Ho + 2 more
'Chee-Wei Tan' 'Lei Wang'] This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs…
Ekaterina Trofimova, Emil Sataev, Andrey Ustyuzhanin, Xiangjie Kong
In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This article introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms…
Pavel Kodytek, Alexandra Bodzas, Jan Zidek, Govind Vashishtha
Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field…
Hina Mahmood, Atif Aftab Ahmed Jilani, Abdul Rauf
Automatic generation of software code from system design models remains an actively explored research area for the past several years. A number of tools are currently available to facilitate and automate the task of generating code from software models. To the best of our knowledge, existing software tools rely on an…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Mohammadreza Nehzati
AI-powered code generation systems available today are ill-suited for deployment in agile software development contexts due to various limitations. The paper proposes a self-healing counterpart framework based on quantum-inspired optimization, biomimetic, and fractal principles to solve these fundamental issues. Our…
Yuan Gao, Yizhou Luo, Wenzhuo Li, Yunquan Lan + 9 more
Laboratory automation enhances experimental throughput and reproducibility, yet widespread adoption is constrained by the expertise required for robotic programming. Here, we introduce LabscriptAI, a multi-agent framework that enables large language models to autonomously generate and validate executable Python scripts…
Yuan Gao, Yizhou Luo, Yunquan Lan, Han Jiang + 2 more
Laboratory automation enhances experimental throughput and reproducibility in synthetic biology, yet widespread adoption remains limited by the programming expertise required to operate robotic platforms. Large Language Models (LLMs) offer promising solutions but lack domain-specific knowledge to program robotics for…
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)…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Pieter Floris Jacobs, Robert Pollice
Scientists across domains are often challenged to master domain-specific languages (DSLs) for their research, which are merely a means to an end but are pervasive in fields like computational chemistry. Automated code generation promises to overcome this barrier, allowing researchers to focus on their core expertise.…
He Jiang
Scientists attending the Yanqi-Lake Meeting during this fall-a summit sponsored by the Chinese Academy of Sciences-discussed the challenges and opportunities of software automation in the big data era. From 11 to 13 October 2018, nearly 40 distinguished scientists from Australia, Britain, Canada, China, Japan…
J. Hérisson, A. N. Hoang, A. El-Sawah, M. M. Khalil + 1 more
In this paper, we present a novel approach to optimizing cell-free protein synthesis (CFPS) systems using artificial intelligence (AI), specifically leveraging ChatGPT-4 for code generation and active learning (AL). This study aims to automate and enhance the process of producing antimicrobial proteins, namely colicin…
Sebastian Fix, Thomas Probst, Oliver Ruggli, Thomas Hanne + 1 more
'Patrik Christen'] Abstract. Combinatorial evolution – the creation of new things through the combination of existing things – can be a powerful way to evolve rather than design technical objects such as electronic circuits. Intriguingly, this seems to be an ongoing and thus open-ended process creating novelty with…
Vishal Gupta, Jesús Irimia, Iván Pau, Alfonso Rodríguez-Patón
The methods to execute biological experiments are evolving. Affordable fluid handling robots and on-demand biology enterprises are making automating entire experiments a reality. Automation offers the benefit of high-throughput experimentation, rapid prototyping and improved reproducibility of results. However…
Dhasarathy Parthasarathy, Yinan Yu, Earl T. Barr
Software development builds digital tools to automate processes, yet its initial phases, up to deployment, remain largely manual. There are two reasons: Development tasks are often under-specified and transitions between tasks usually require a translator. These reasons are mutually reinforcing: it makes little sense…
Authors not listed
Natural language processing with the help of large language models such as ChatGPT has become ubiquitous in many software applications and allows users to interact even with complex hardware or software in an intuitive way. The recent concepts of Self-Driving Labs and Material Acceleration Platforms stand to benefit…
Wenyu Zhang, Mason Guy, Jerrica Yang, Lucy Hao + 5 more
Large Language Models (LLMs) have revolutionized numerous industries as well as accelerated scientific research. However, their application in planning and conducting experimental science, has been limited. In this study, we introduce an adaptable prompt-set with GPT-4, converting literature experimental procedures…
Hongda Jiang, Jahziel Chase, Lianxiang Fu, Sathvik Shivaram + 1 more
This study presents a case analysis of using AI systems as co-pilots in biological research, focusing on functional protein networks in invasive colorectal cancer. We used public proteomic data alongside ChatGPT, GitHub Copilot, and PaperQA to automate parts of the workflow, including literature review, code…