Ka Ching Chan

Ka Ching Chan

Generative AI is changing how research software is developed, but rapid AI-assisted development can weaken continuity, traceability, and methodological clarity. SHAPR (Solo, Human-centred, AI-assisted PRactice) was proposed as a framework for structuring AI-assisted research software development. This paper presents a documented case of applying SHAPR to the development of a modular share trading system. From the outset, the project adopted a SHAPR-informed working configuration that shaped how interaction, implementation, and documentation were organised. Across iterative development cycles, the project generated a structured evidence base including reflection notes, development cycle review notes, source-of-truth documents, contracts, quick captures, workflow notes, and evolving code artefacts. The case showed that continuous documentation updates, supported by quick capture and AI-assisted refinement, helped maintain organised and usable project knowledge throughout development. Five recurring lessons were identified: contracts stabilised AI-assisted coding, a maintained source-of-truth layer improved coherence, cycle-boundary snapshots strengthened continuity, code and documentation co-evolved through quick capture and iterative refinement, and environment setup itself contributed to knowledge generation. The case also illustrates a practical SHAPR operating configuration in which a ChatGPT Project and cycle-specific chats supported interaction, reasoning, summarisation, and coding collaboration, PyCharm supported artefact implementation, and Obsidian supported external working memory, structured documentation, reflection, continuity, and repository-oriented note organisation, while remaining consistent with SHAPR's tool-agnostic principle. The paper contributes practical guidance and good practices for researchers conducting AI-assisted research software development.

Ka Ching Chan

SHAPR (Solo Human-Centred and AI-Assisted Practice) is a framework for research software development that integrates human-centred decision-making with AI-assisted capabilities. While prior work introduced SHAPR as a conceptual framework, this paper focuses on its operationalisation as a structured, traceable, and knowledge-generating approach to AI-assisted research practice. We present a set of interconnected models describing how research activities are organised through iterative cycles (Explore-Build-Use-Evaluate-Learn), how artefacts evolve through development and use, and how empirical evidence is transformed into conceptual knowledge. Central to this process are Structured Knowledge Units (SKUs), which provide modular and reusable representations of insights derived from practice, supporting knowledge accumulation across cycles. The framework introduces evidence and traceability as a cross-cutting mechanism linking human decisions, AI-assisted development, and artefact evolution to enable transparency, reproducibility, and systematic refinement. SHAPR is also positioned as an AI-executable research framework, as its structured processes and documentation can be interpreted by generative AI systems to guide research workflows. Simultaneously, SHAPR supports a continuum of AI involvement, allowing researchers to balance control, learning, and automation across different contexts. Beyond individual workflows, SHAPR is conceptualised as an integrated research system combining LLM workspaces, development environments, cloud storage, and version control to support scalable, knowledge-centred research practices. Overall, SHAPR provides a practical and theoretically grounded foundation for conducting rigorous, transparent, and reproducible research in AI-assisted environments, contributing to the development of scalable and methodologically sound research practices.