Paradigm shift on Coding Productivity Using GenAI

• URL: http://arxiv.org/abs/2504.18404v1
• Date: Fri, 25 Apr 2025 15:00:06 GMT
• Title: Paradigm shift on Coding Productivity Using GenAI
• Authors: Liang Yu,
• Abstract summary: Generative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation.<n>This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and domains.
• Score: 3.7117429391624803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation. However, empirical evidence on GenAI's productivity effects in industrial settings remains limited. This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and FinTech domains. Through surveys and interviews with industrial domain-experts, we identify primary productivity-influencing factors, including task complexity, coding skills, domain knowledge, and GenAI integration. Our findings indicate that GenAI tools enhance productivity in routine coding tasks (e.g., refactoring and Javadoc generation) but face challenges in complex, domain-specific activities due to limited context-awareness of codebases and insufficient support for customized design rules. We highlight new paradigms for coding transfer, emphasizing iterative prompt refinement, immersive development environment, and automated code evaluation as essential for effective GenAI usage.

Related papers

• Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
  computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.<n>We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.<n>We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
  arXiv  Detail & Related papers  (2025-02-18T02:26:50Z)
• GenAIOps for GenAI Model-Agility [2.7396907658239424]
  We discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions. First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models. We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools.
  arXiv  Detail & Related papers  (2024-12-19T03:29:03Z)
• Generating a Low-code Complete Workflow via Task Decomposition and RAG [0.040964539027092926]
  GenAI-based systems are more difficult to design due to their scale and versatility.<n>We formalize two techniques, Task Decomposition and Retrieval-Augmented Generation, as design patterns for GenAI-based systems.<n>As these two patterns affect the entire AI development cycle, we explain how they impacted the dataset creation, model training, model evaluation, and deployment phases.
  arXiv  Detail & Related papers  (2024-11-29T20:13:56Z)
• Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing [0.9668407688201359]
  generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing.<n>We developed a tool, CodeScribe, which combines prompt engineering with user supervision to establish an efficient process for code conversion.<n>We also address the challenges of AI-driven code translation and highlight its benefits for enhancing productivity in scientific computing.
  arXiv  Detail & Related papers  (2024-10-31T16:48:41Z)
• Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
  We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E) We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
  arXiv  Detail & Related papers  (2024-07-09T23:13:26Z)
• UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models [88.16197692794707]
  UniGen is a comprehensive framework designed to produce diverse, accurate, and highly controllable datasets. To augment data diversity, UniGen incorporates an attribute-guided generation module and a group checking feature. Extensive experiments demonstrate the superior quality of data generated by UniGen.
  arXiv  Detail & Related papers  (2024-06-27T07:56:44Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models [54.58108387797138]
  We investigate the effectiveness of prompt learning in code intelligence tasks. Existing automatic prompt design methods are very limited to code intelligence tasks. We propose Genetic Auto Prompt (GenAP) which utilizes an elaborate genetic algorithm to automatically design prompts.
  arXiv  Detail & Related papers  (2024-03-20T13:37:00Z)
• Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflow [2.6124032579630114]
  Generative AI (GenAI) has fundamentally changed how knowledge workers, such as software developers, solve tasks and collaborate to build software products. Introducing innovative tools like ChatGPT and Copilot has created new opportunities to assist and augment software developers across various problems. Our study reveals that ChatGPT signifies a paradigm shift in the workflow of software developers. The technology empowers developers by enabling them to work more efficiently, speed up the learning process, and increase motivation by reducing tedious and repetitive tasks.
  arXiv  Detail & Related papers  (2024-02-12T12:36:29Z)
• Exploring the intersection of Generative AI and Software Development [0.0]
  The synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
  arXiv  Detail & Related papers  (2023-12-21T19:23:23Z)
• Investigating Explainability of Generative AI for Code through Scenario-based Design [44.44517254181818]
  generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. We conduct 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.
  arXiv  Detail & Related papers  (2022-02-10T08:52:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.