Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
95 tokens/sec
Gemini 2.5 Pro Premium
55 tokens/sec
GPT-5 Medium
20 tokens/sec
GPT-5 High Premium
20 tokens/sec
GPT-4o
98 tokens/sec
DeepSeek R1 via Azure Premium
86 tokens/sec
GPT OSS 120B via Groq Premium
475 tokens/sec
Kimi K2 via Groq Premium
225 tokens/sec
2000 character limit reached

Flexible Invariants Through Semantic Collaboration (1311.6329v2)

Published 25 Nov 2013 in cs.SE

Abstract: Modular reasoning about class invariants is challenging in the presence of dependencies among collaborating objects that need to maintain global consistency. This paper presents semantic collaboration: a novel methodology to specify and reason about class invariants of sequential object-oriented programs, which models dependencies between collaborating objects by semantic means. Combined with a simple ownership mechanism and useful default schemes, semantic collaboration achieves the flexibility necessary to reason about complicated inter-object dependencies but requires limited annotation burden when applied to standard specification patterns. The methodology is implemented in AutoProof, our program verifier for the Eiffel programming language (but it is applicable to any language supporting some form of representation invariants). An evaluation on several challenge problems proposed in the literature demonstrates that it can handle a variety of idiomatic collaboration patterns, and is more widely applicable than the existing invariant methodologies.

Citations (37)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube