Fabian Meyer, Marcel Hark, Jürgen Giesl
We present a novel modular approach to infer upper bounds on the expected runtime of probabilistic integer programs automatically. To this end, it computes bounds on the runtime of program parts and on the sizes of their variables in an alternating way. To evaluate its power, we implemented our approach in a new version of our open-source tool KoAT.
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We study weakest precondition reasoning about the (co)variance of outcomes and the variance of run-times of probabilistic programs with conditioning. For outcomes, we show that approximating (co)variances is computationally more difficult than approximating expected values. In particular, we prove that computing both lower and upper bounds for (co)variances is $Σ^{0}_{2}$-complete. As a consequence, neither lower nor upper bounds are computably enumerable. We therefore present invariant-based techniques that do enable enumeration of both upper and lower bounds, once appropriate invariants are …
We present a technique to infer lower bounds on the worst-case runtime complexity of integer programs, where in contrast to earlier work, our approach is not restricted to tail-recursion. Our technique constructs symbolic representations of program executions using a framework for iterative, under-approximating program simplification. The core of this simplification is a method for (under-approximating) program acceleration based on recurrence solving and a variation of ranking functions. Afterwards, we deduce asymptotic lower bounds from the resulting simplified programs using a special-purpo…
Dependency Pairs for Expected Innermost Runtime Complexity and Strong Almost-Sure Termination of Probabilistic Term Rewriting
本文提出首个用于概率项重写系统最内层重写的依赖对框架，以分析期望复杂度并证明强几乎必然终止，并在AProVE中实现，展示了其优势。
We are concerned with the average case runtime complexity analysis of a prototypical imperative language endowed with primitives for sampling and probabilistic choice. Taking inspiration from known approaches from to the modular resource analysis of non-probabilistic programs, we investigate how a modular runtime analysis is obtained for probabilistic programs.
Essential tasks for the verification of probabilistic programs include bounding expected outcomes and proving termination in finite expected runtime. We contribute a simple yet effective inductive synthesis approach for proving such quantitative reachability properties by generating inductive invariants on source-code level. Our implementation shows promise: It finds invariants for (in)finite-state programs, can beat state-of-the-art probabilistic model checkers, and is competitive with modern tools dedicated to invariant synthesis and expected runtime reasoning.
We study termination time and recurrence time in programs with unbounded recursion, which are either randomized or operate on some statistically quantified inputs. As the underlying formal model for such programs we use probabilistic pushdown automata (pPDA) which are equivalent to probabilistic recursive state machines. We obtain tail bounds for the distribution of termination time for pPDA. We also study the recurrence time for probabilistic recursive programs that are not supposed to terminate (such as system daemons, network servers, etc.). Typically, such programs react to certain request…
SENTIL: A Runtime Verification Tool for Probabilistic Temporal Logic
本文提出SENTIL，一种具有统计保证的新型运行时验证工具，用于验证以概率信号时序逻辑（PrSTL）表达的要求，通过统计模型检查、序列概率比率检验和自适应稀有事件估计提供严格的概率保证，并在多个场景中展示了性能提升。
In this abstract we study the resource consumption of quantum programs. Specifically, we focus on the expected runtime of programs and, inspired by recent methods for probabilistic programs, we develop a calculus à la weakest precondition to formally and systematically derive the (exact) expected runtime of quantum programs. Notably, the calculus admits a notion of loop runtime invariant that can be readily used to derive upper bounds of their runtime. Finally, we show the applicability of our calculus analyzing the runtime of (a simplified version of) the BB84 quantum key distribution protoco…
On Certificates, Expected Runtimes, and Termination in Probabilistic Pushdown Automata
本文研究概率下推自动机定量界证书与期望终止时间的关系，借助底层多项式方程组 Jacobian 的谱半径等性质，证明正几乎必然终止总存在易验证证书。
We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) a…
This paper is an experimental exploration of the relationship between the runtimes of Turing machines and the length of proofs in formal axiomatic systems. We compare the number of halting Turing machines of a given size to the number of provable theorems of first-order logic of a given size, and the runtime of the longest-running Turing machine of a given size to the proof length of the most-difficult-to-prove theorem of a given size. It is suggested that theorem provers are subject to the same non-linear tradeoff between time and size as computer programs are, affording the possibility of de…
To Zip Through the Cost Analysis of Probabilistic Programs
针对概率程序期望运行时间自动化推理有限的问题，提出Liquid Haskell中精化类型的概率单子，支持离散及无限分布分析，通过四个案例验证其自动化与交互式证明集成能力，含拉链树期望运行时间首证。
This paper presents a wp-style calculus for obtaining expectations on the outcomes of (mutually) recursive probabilistic programs. We provide several proof rules to derive one-- and two--sided bounds for such expectations, and show the soundness of our wp-calculus with respect to a probabilistic pushdown automaton semantics. We also give a wp-style calculus for obtaining bounds on the expected runtime of recursive programs that can be used to determine the (possibly infinite) time until termination of such programs.
A New Syntax and Semantics for Probabilistic Trace Expressions
本文针对部分可观测运行时验证问题，提出概率迹表达式（PTEs）的新语法与语义，改进原PTE的概率关联方式，支持间隙推理与信念监测，涵盖隐马尔可夫模型，通过火星车场景验证其应用。
One of the main reasons to employ a description logic such as EL or EL++ is the fact that it has efficient, polynomial-time algorithmic properties such as deciding consistency and inferring subsumption. However, simply by adding negation of concepts to it, we obtain the expressivity of description logics whose decision procedure is {ExpTime}-complete. Similar complexity explosion occurs if we add probability assignments on concepts. To lower the resulting complexity, we instead concentrate on assigning probabilities to Axioms (GCIs). We show that the consistency detection problem for such a pr…
Exact Bayesian Inference for Loopy Probabilistic Programs using Generating Functions
本文提出基于概率生成函数语义的精确贝叶斯推断方法，并实现 Prodigy 工具，以支持含无界循环的条件化概率程序的推断与定量验证。
We present a new inductive rule for verifying lower bounds on expected values of random variables after execution of probabilistic loops as well as on their expected runtimes. Our rule is simple in the sense that loop body semantics need to be applied only finitely often in order to verify that the candidates are indeed lower bounds. In particular, it is not necessary to find the limit of a sequence as in many previous rules.
A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale Programs
Preguss通过结合静态分析与演绎验证，实现大规模程序的自动化形式化验证，显著提升验证效率。
Probabilistic programs are typically normal-looking programs describing posterior probability distributions. They intrinsically code up randomized algorithms and have long been at the heart of modern machine learning and approximate computing. We explore the theory of generating functions [19] and investigate its usage in the exact quantitative reasoning of probabilistic programs. Important topics include the exact representation of program semantics [13], proving exact program equivalence [5], and -- as our main focus in this extended abstract -- exact probabilistic inference. In probabilisti…
Polyhedra form an established abstract domain for inferring runtime properties of programs using abstract interpretation. Computations on them need to be certified for the whole static analysis results to be trusted. In this work, we look at how far we can get down the road of a posteriori verification to lower the overhead of certification of the abstract domain of polyhedra. We demonstrate methods for making the cost of inclusion certificate generation negligible. From a performance point of view, our single-representation, constraints-based implementation compares with state-of-the-art impl…
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