[Paper] Proto-ML: An IDE for ML Solution Prototyping

Source: arXiv - 2602.21734v1

Overview

The paper presents Proto‑ML, a purpose‑built Integrated Development Environment (IDE) that streamlines the entire lifecycle of machine‑learning (ML) prototyping. By weaving together implementation, analysis, and knowledge‑management tools, Proto‑ML tackles the chronic pain points of fragmented workflows, poor stakeholder visibility, and lost reusable artefacts.

Key Contributions

• Unified IDE architecture composed of three interchangeable extension bundles (implementation, analysis, knowledge‑management).
• Structured documentation model that captures design decisions, evaluation criteria, and stakeholder feedback directly alongside code.
• Cross‑project knowledge reuse mechanisms (template libraries, searchable provenance records) that let teams seed new prototypes with proven components.
• Stakeholder‑centric evaluation features that surface quality metrics and requirement checklists to non‑technical contributors.
• Preliminary usability study showing measurable gains in prototyping speed and perceived transparency.

Methodology

The authors built Proto‑ML as a set of plug‑ins for a mainstream IDE (e.g., VS Code). Each plug‑in adds a lightweight UI pane:

1. Prototype Implementation Bundle – standard code editor plus scaffolding generators for common ML tasks (data loading, model definition, training loops).
2. Analysis Bundle – integrates automated checks (e.g., data‑drift detection, model‑performance dashboards) and lets users define custom quality criteria.
3. Knowledge‑Management Bundle – records artefacts (datasets, hyper‑parameters, evaluation reports) in a project‑wide knowledge graph; supports tagging, versioning, and search across projects.

The team evaluated the system with a small cohort of data scientists and domain experts (≈8 participants) who used Proto‑ML to build a sentiment‑analysis prototype. They measured task completion time, number of documentation artefacts created, and collected qualitative feedback via questionnaires.

Results & Findings

• 30 % reduction in average time to reach a “first viable prototype” compared with a baseline workflow using separate tools.
• Participants produced twice as many documented evaluation checkpoints, indicating richer traceability.
• 85 % of users reported that the knowledge‑management view helped them locate reusable components they would otherwise have rebuilt.
• Stakeholders (non‑technical product owners) felt more included, citing the visual quality‑criteria dashboard as a bridge to the technical work.

Practical Implications

• Faster iteration cycles: Development teams can spin up and evaluate prototypes without hopping between notebooks, CLI scripts, and external dashboards.
• Reduced duplication: Organizations can build a living catalogue of vetted preprocessing pipelines, model architectures, and evaluation scripts that new projects can import instantly.
• Better governance & compliance: The built‑in documentation and provenance tracking simplify audit trails for regulated domains (finance, healthcare).
• Cross‑functional collaboration: Product managers, UX designers, and data engineers can all view and comment on the same artefacts, aligning expectations early.
• Tool‑agnostic extensibility: Because the bundles are plug‑ins, teams can adopt Proto‑ML alongside their existing stack (Jupyter, PyCharm, etc.) rather than being forced into a monolithic platform.

Limitations & Future Work

• Small user study: The evaluation involved a limited number of participants and a single use case, so broader generalizability remains unproven.
• Integration depth: Current prototypes rely on manual configuration for some external services (e.g., cloud‑based data stores), which could hinder adoption in large‑scale pipelines.
• Scalability of the knowledge graph: As the repository of artefacts grows, performance of search and retrieval may degrade; the authors plan to explore more robust indexing strategies.
• Extending stakeholder features: Future work includes richer role‑based access controls and automated summarization of technical artefacts for non‑technical audiences.

Proto‑ML offers a promising step toward making ML prototyping a first‑class, collaborative activity—turning what is often a chaotic, siloed process into a repeatable, transparent workflow that developers and product teams can both trust.

Authors

• Selin Coban
• Miguel Perez
• Horst Lichter

Paper Information

• arXiv ID: 2602.21734v1
• Categories: cs.SE
• Published: February 25, 2026
• PDF: Download PDF