Speakers
Description
This work presents a practical and efficient approach to accelerating the development of the Scilab Control System Toolbox through AI-assisted workflows. The focus is on reimplementing key control system functions from GNU Octave within the Scilab environment, thereby enhancing native capabilities of Scilab.
Traditionally, the development of a single function, including code implementation, test case creation, and documentation, could require up to two weeks. With the integration of AI-assisted tools, this process can be reduced to a matter of hours, enabling significantly faster development while maintaining reliability and functional accuracy.
The methodology involves analyzing existing Octave functions, generating corresponding Scilab functions using AI, followed by expert review, validation against Octave outputs, and refinement where necessary. While AI expedites the development process, human oversight remains essential to guide the workflow and ensure correctness, as AI-generated code may contain errors or incomplete logic.
To date, approximately 50 out of a targeted 150 functions have been successfully reimplemented using this approach. The work contributes to the open-source community by broadening access to advanced control system tools in Scilab and demonstrates the potential of combining AI capabilities with domain expertise to accelerate scientific software development. The same methodology can be extended to other toolboxes and domains within the open-source ecosystem.
Keywords: Scilab, GNU Octave, Control Systems, AI-assisted Development, Open Source Software, Scientific Computing
Session author's bio
Rashmi Rajan Patankar is an open-source contributor and developer, specializing in scientific computing and control systems. She has been actively involved in enhancing the Scilab Control System Toolbox and integrating AI-assisted workflows into its development process. With experience in Scilab, GNU Octave, and open-source collaboration, Rashmi focuses on bridging academic tools and community-driven software. Her work emphasizes accelerating function development, maintaining software reliability. She is passionate about fostering AI-driven innovation in the open-source scientific computing ecosystem.
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