Statistical Model Checking Technique Research Articles

Statistical Model Checking in Process Mining: A Comprehensive Approach to Analyse Stochastic Processes

The study of business process analysis and optimisation has attracted significant scholarly interest in the recent past, due to its integral role in boosting organisational performance. A specific area of focus within this broader research field is process mining (PM). Its purpose is to extract knowledge and insights from event logs maintained by information systems, thereby discovering process models and identifying process-related issues. On the other hand, statistical model checking (SMC) is a verification technique used to analyse and validate properties of stochastic systems that employs statistical methods and random sampling to estimate the likelihood of a property being satisfied. In a seamless business setting, it is essential to validate and verify process models. The objective of this paper is to apply the SMC technique in process mining for the verification and validation of process models with stochastic behaviour and large state space, where probabilistic model checking is not feasible. We propose a novel methodology in this research direction that integrates SMC and PM by formally modelling discovered and replayed process models and apply statistical methods to estimate the results. The methodology facilitates an automated and proficient evaluation of the extent to which a process model aligns with user requirements and assists in selecting the optimal model. We demonstrate the effectiveness of our methodology with a case study of a loan application process performed in a financial institution that deals with loan applications submitted by customers. The case study highlights our methodology’s capability to identify the performance constraints of various process models and aid enhancement efforts.

AFMT: Maintaining the safety-security of industrial control systems

Modern day industrial control systems are overwhelmingly complex. These systems feature intricate interactions between the cyber and the physical components. At the same time, they need to be trustworthy and deliver their services continuously. Underpinning, a crucial industrial activity to ensure the dependability of such critical systems is through timely maintenance, inspections and repairs. Several strategies exist here: “fix it when it breaks” (reactive maintenance), monitor and maintain a system in pre-established time intervals (preventive maintenance), preventive action based upon detected symptoms of failures condition-based maintenance (CBM), etc. In literature, the question of optimal maintenance frequency have been a subject of intense study. However, most papers, do not take information security aspects into account. This paper provides an automated tool-supported quantitative risk analysis framework, Attack-Fault-Maintenance Trees, AFMTs, that will enable practitioners to make informed choice on: (a) identifying the critical component(s) necessary for uninterrupted systems; (b) a decision support system that will provide informed choices on policy measures, countermeasures and safeguards that will reduce the disruptions; (c) run the “what-if” scenarios to find the optimal trade-offs between system attributes (safety, security, usability and maintenance). The front-end of the tool is a domain-specific language geared to represent the system architecture using graphical-constructs. The back-end of the framework remains hidden to the practitioner. It consists of a mathematical engine based on statistical model-checking techniques. A case study of oil-pipeline is used to demonstrate the efficacy of our framework.

A Survey of Statistical Model Checking

Interactive, distributed, and embedded systems often behave stochastically, for example, when inputs, message delays, or failures conform to a probability distribution. However, reasoning analytically about the behavior of complex stochastic systems is generally infeasible. While simulations of systems are commonly used in engineering practice, they have not traditionally been used to reason about formal specifications. Statistical model checking (SMC) addresses this weakness by using a simulation-based approach to reason about precise properties specified in a stochastic temporal logic. A specification for a communication system may state that within some time bound, the probability that the number of messages in a queue will be greater than 5 must be less than 0.01. Using SMC, executions of a stochastic system are first sampled, after which statistical techniques are applied to determine whether such a property holds. While the output of sample-based methods are not always correct, statistical inference can quantify the confidence in the result produced. In effect, SMC provides a more widely applicable and scalable alternative to analysis of properties of stochastic systems using numerical and symbolic methods. SMC techniques have been successfully applied to analyze systems with large state spaces in areas such as computer networking, security, and systems biology. In this article, we survey SMC algorithms, techniques, and tools, while emphasizing current limitations and tradeoffs between precision and scalability.

Schedulability of Herschel revisited using statistical model checking

Schedulability analysis is a main concern for several embedded applications due to their safety-critical nature. The classical method of response time analysis provides an efficient technique used in industrial practice. However, the method is based on conservative assumptions related to execution and blocking times of tasks. Consequently, the method may falsely declare deadline violations that will never occur during execution. This paper is a continuation of previous work of the authors in applying extended timed automata model checking (using the tool UPPAAL) to obtain more exact schedulability analysis, here in the presence of non-deterministic computation times of tasks given by intervals [BCET,WCET]. Computation intervals with preemptive schedulers make the schedulability analysis of the resulting task model undecidable. Our contribution is to propose a combination of model checking techniques to obtain some guarantee on the (un)schedulability of the model even in the presence of undecidability. Two methods are considered: symbolic model checking and statistical model checking. Since the model uses stop-watches, the reachability problem becomes undecidable so we are using an over-approximation technique. We can safely conclude that the system is schedulable for varying values of BCET. For the cases where deadlines are violated, we use polyhedra to try to confirm the witnesses. Our alternative method to confirm non-schedulability uses statistical model-checking (SMC) to generate counter-examples that are always realizable. Another use of the SMC technique is to do performance analysis on schedulable configurations to obtain, e.g., expected response times. The methods are demonstrated on a complex satellite software system yielding new insights useful for the company.

MDM: A Mode Diagram Modeling Framework

Periodic control systems used in spacecrafts and automotives are usually period-driven and can be decomposed into different modes with each mode representing a system state observed from outside. Such systems may also involve intensive computing in their modes. Despite the fact that such control systems are widely used in the above-mentioned safety-critical embedded domains, there is lack of domain-specific formal modelling languages for such systems in the relevant industry. To address this problem, we propose a formal visual modeling framework called mode diagram as a concise and precise way to specify and analyze such systems. To capture the temporal properties of periodic control systems, we provide, along with mode diagram, a property specification language based on interval logic for the description of concrete temporal requirements the engineers are concerned with. The statistical model checking technique can then be used to verify the mode diagram models against desired properties. To demonstrate the viability of our approach, we have applied our modelling framework to some real life case studies from industry and helped detect two design defects for some spacecraft control systems.

The stochastic semantics and verification for periodic control systems

Periodic control systems (PCS) are widely used in the embedded industry like aerospace and automotive. Such systems usually run periodic tasks and respond to the external signals. Based on our previous work on Mode diagram modeling (MDM) notations for specifying the periodic control system, we present the stochastic semantics for MDM in this paper. The stochastic semantics of MDM is based on the Markov chain. The semantics proposed here provides the basis for the satisfaction of formulae of the interval temporal logic (ITL) based specification language that is aimed to specify the properties of PCS. To verify whether the system satisfies the ITL-based properties, we apply the statistical model checking technique to efficiently estimate the probability of the system satisfying the given property with a desired level of confidence. The empirical experiments show that our approach is both effective and efficient.