[Paper] Fancy Some Chips for Your TeaStore? Modeling the Control of an Adaptable Discrete System

Source: arXiv - 2512.23496v1

Overview

The paper introduces Chips, a domain‑specific language that blends ideas from control theory and mainstream programming to help developers model, compose, and verify complex, resource‑constrained web applications. Using a variant of the classic “TeaStore” example, the authors demonstrate how Chips can capture both functional behavior and non‑functional constraints (e.g., hardware limits, network latency) in a single, analyzable model.

Key Contributions

• Chips language: A lightweight DSL for describing component‑based systems as functional blocks with explicit control‑theoretic semantics.
• Unified modeling of resources: Enables developers to encode software, hardware, and network constraints alongside business logic.
• Systematic design workflow: Provides a step‑by‑step methodology (specify → compose → analyze → refine) that can be applied to real‑world web services.
• Formal analysis support: Shows how existing model‑checking tools can be leveraged to verify robustness properties (dead‑lock freedom, QoS guarantees).
• Running example (Adaptable TeaStore): Demonstrates the language’s expressiveness by modeling a dynamic e‑commerce service that can reconfigure itself when resources change.

Methodology

1. Functional Block Definition – Developers write blocks that expose inputs, outputs, and internal state. Each block is annotated with control parameters (e.g., maximum request rate, memory budget).
2. Composition Operators – Chips supplies combinators (parallel, sequential, conditional) to wire blocks together, forming a system graph that mirrors the architecture of the target application.
3. Constraint Embedding – Resource constraints are expressed as linear inequalities or temporal predicates attached to blocks or connections.
4. Model Extraction – The composed graph is automatically translated into a formal model (e.g., a labeled transition system) compatible with off‑the‑shelf verification tools.
5. Analysis & Refinement – Using model checking or simulation, developers assess properties such as robustness (ability to meet QoS under varying loads) and adaptability (safe reconfiguration). Violations trigger iterative refinements of the Chips specification.

The entire pipeline is designed to be developer‑friendly: the DSL syntax resembles familiar imperative languages, and the toolchain integrates with standard build systems.

Results & Findings

• Expressiveness: The TeaStore case study required modeling of dynamic pricing, inventory updates, and fallback micro‑services. Chips captured all these aspects with fewer than 150 lines of specification, compared to several hundred lines in a traditional Petri‑net model.
• Verification Success: Model checking confirmed that the system never enters a dead‑locked state, even when a critical micro‑service becomes unavailable.
• Performance Overhead: Translating a Chips model to a transition system incurred modest computational cost (≈ 2 seconds for the TeaStore model), making it practical for iterative development cycles.
• Robustness Guarantees: By adjusting control parameters, the authors showed that the system could automatically throttle request rates to stay within CPU and bandwidth limits, preserving response‑time SLAs.

Practical Implications

• Rapid Prototyping of Adaptive Services – Teams can prototype self‑adjusting web applications (e.g., auto‑scaling e‑commerce sites, IoT gateways) while having formal guarantees about safety and QoS.
• Unified Resource Management – Instead of scattering resource checks across codebases, developers embed constraints directly in the model, reducing bugs caused by inconsistent handling.
• Integration with CI/CD – The verification step can be scripted into continuous integration pipelines, catching adaptation‑related regressions early.
• Cross‑Team Collaboration – Because Chips abstracts away low‑level implementation details, product managers, architects, and developers can discuss system behavior using a common, mathematically grounded language.

Limitations & Future Work

• Scalability – While the prototype handles medium‑size examples, the state‑space explosion problem may limit analysis of very large micro‑service ecosystems without additional abstraction techniques.
• Tooling Maturity – The current toolchain is a research prototype; richer IDE support (syntax highlighting, auto‑completion) and tighter integration with popular frameworks (Spring, Node.js) are still pending.
• Dynamic Runtime Adaptation – The paper focuses on design‑time verification; extending Chips to generate runtime monitors or controllers that enforce the verified policies in production is an open direction.
• Empirical Evaluation – Future work includes user studies with industry developers to assess usability and impact on development velocity.

Bottom line: Chips offers a promising bridge between control‑theoretic rigor and everyday software engineering, giving developers a practical way to model, verify, and adapt resource‑constrained web applications before they ship.

Authors

• Anna Gallone
• Simon Bliudze
• Sophie Cerf
• Olga Kouchnarenko

Paper Information

• arXiv ID: 2512.23496v1
• Categories: cs.PL, cs.DC
• Published: December 29, 2025
• PDF: Download PDF