Three Algorithms for Interface Synthesis: A Comparative Study

A temporal interface for a system component is a finite automaton that specifies the legal sequences of input events. We evaluate and compare three different algorithms for automatically extracting the temporal interface from the transition graph of a component: (1) a game algorithm that computes the interface as a representation of the most general environment strategy to avoid a safety violation; (2) a learning algorithm that repeatedly queries the component to construct the minimal interface automaton; and (3) a CEGAR algorithm that iteratively refines an abstract interface hypothesis by adding relevant state information from the component. Since algorithms (2) and (3) have been published in different software contexts, for comparison purposes, we present the three algorithms in a uniform finite-state setting. We furthermore extend the three algorithms to construct maximally permissive interface automata, which accept all legal input sequences. While the three algorithms have similar worst-case complexities, their actual running times differ greatly depending on the component whose interface is computed. On the theoretical side, we provide families of components that exhibit exponential differences in the performance of the three algorithms. On the practical side, we evaluate the three algorithms experimentally on a variety of real world examples. Not surprisingly, the experimental evaluation confirms the theoretical expectation: learning performs best if the minimal interface automaton is small; CEGAR performs best if only few component variables are needed to prove an interface hypothesis safe and permissive; and the direct (game) algorithm outperforms both approaches if neither is the case

Abstraction and Learning for Infinite-State Compositional Verification

Despite many advances that enable the application of model checking techniques to the verification of large systems, the state-explosion problem remains the main challenge for scalability. Compositional verification addresses this challenge by decomposing the verification of a large system into the verification of its components. Recent techniques use learning-based approaches to automate compositional verification based on the assume-guarantee style reasoning. However, these techniques are only applicable to finite-state systems. In this work, we propose a new framework that interleaves abstraction and learning to perform automated compositional verification of infinite-state systems. We also discuss the role of learning and abstraction in the related context of interface generation for infinite-state components.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

Automated Assume-Guarantee Reasoning by Abstraction Refinement

Current automated approaches for compositional model checking in the assume-guarantee style are based on learning of assumptions as deterministic automata. We propose an alternative approach based on abstraction refinement. Our new method computes the assumptions for the assume-guarantee rules as conservative and not necessarily deterministic abstractions of some of the components, and refines those abstractions using counter-examples obtained from model checking them together with the other components. Our approach also exploits the alphabets of the interfaces between components and performs iterative refinement of those alphabets as well as of the abstractions. We show experimentally that our preliminary implementation of the proposed alternative achieves similar or better performance than a previous learning-based implementation

Algorithms for Interface Synthesis

A temporal interface for a software component is a finite automaton that specifies the legal sequences of calls to functions that are provided by the component. We compare and evaluate three different algorithms for automatically extracting temporal interfaces from program code: (1) a game algorithm that computes the interface as a representation of the most general environment strategy to avoid a safety violation; (2) a learning algorithm that repeatedly queries the program to construct the minimal interface automaton; and (3) a CEGAR algorithm that iteratively refines an abstract interface hypothesis by adding relevant program variables. For comparison purposes, we present and implement the three algorithms in a unifying formal setting. While the three algorithms compute the same output and have similar worst-case complexities, their actual running times may differ considerably for a given input program. On the theoretical side, we provide for each of the three algorithms a family of input programs on which that algorithm outperforms the two alternatives. On the practical side, we evaluate the three algorithms experimentally on a variety of Java libraries