23 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…
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…
Gabriel Vargas-Monroy, Daissi-Bibiana Gonzalez-Roldan, Carlos Enrique Montenegro-Marín, Alejandro-Paolo Daza-Corredor + 1 more
7## Discussion The results of this study highlight the effectiveness of using computer vision and deep learning techniques for the automated generation of source code from architectural diagrams. The system effectively bridges the gap between the design and implementation phases by leveraging Model-Driven Architecture…
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…
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…
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…
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…
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.…
Elena Domínguez-Romero, Stanislav Mazurenko, Martin Scheringer, Vítor A P Martins dos Santos + 5 more
'Vítor A P Martins dos Santos' 'Chris T Evelo' 'Mihail Anton' 'John M Hancock' 'Anže Županič' 'Maria Suarez-Diez'] Dear colleagues, We thank you for your comments on our manuscript [1] and for raising the important question of automation [2]. The publications by Sepp et al. (2019) [3] and Liu et al. (2024) [4] focus on…
Hyeon-Min Kim, Hwayeon Jeong, Abyot Melkamu Mekonnen, Yeongjun Kim + 4 more
Large language models (LLMs) are increasingly used to generate bioinformatics pipelines and to carry out analyses from natural-language prompts. However, the resulting analyses are often difficult to reproduce across sessions, owing to the non-deterministic nature of LLM-driven conversations and heterogeneity of local…
Ihsan Tolga Medeni, Metehan Ünal, Roberto Galizi, Bryan Bartley + 4 more
Large language models have transformed software engineering practices. However, generated artefacts are not always developer-friendly and may partially meet complex requirements. As the need to standardise, integrate, and develop tools in engineering biology increases, novel approaches are needed to create and maintain…
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…
Junlong Peng, Xiangjun Liu
Automated code compliance checking plays an important role in moving the construction industry forward. While traditional drawing review relies on the identification of industry experts, the purpose of this study is to realize automatic code review by using BIM technology and knowledge graph technology. The method is…