Efficient State Identification for Finite State Machine-Based Testing

This paper presents a state identification approach which eliminates ambiguities caused by sensing and uncertainty. In manipulation tasks, the identification of contact states based on force and position sensing is affected by ambiguities. The approach uses recurrent neural networks to learn an internal representation of the finite state automata defined by the sensor patterns. The technique is demonstrated using a simulated learning task. State identification is combined with other techniques in a sensor-based learning architecture for robotic manipulation. Results are presented for simulated tasks. The system is able to manage ambiguous states which previously were impossible to learn.

The Extended Finite State Machine (EFSM) is a commonly used model for specifying software systems. A test sequence for an EFSM is a sequence composed of values of input variables, which can make the EFSM "execute" along a complete path from entry to exit. Traditional test sequence generation methods for EFSM almost imitate those FSM-based approaches and focus on states identification. Most of them impose significant restrictions on the EFSM. This paper proposes a path-oriented approach to generating test cases for EFSM and presents a tool for test data generation. The experiments show that our tool can generate executable test sequences for EFSM models of software systems automatically in acceptable time.

This paper is devoted to study the use of 'gedanken' experiments with Finite State Machines (FSMs) for protocol passive testing optimization. We discuss how the knowledge obtained from the state identification of an implementation under test (IUT) can be utilized for effective IUT monitoring. Differently from active testing techniques, such identification is performed by only observing the IUT behavior. If the state identification is possible (at least partially), then this fact allows to reduce the number of properties (test purposes) to be checked at certain execution point(s). Correspondingly, this allows to simplify and/or accelerate, i.e. improve the monitoring process by verifying the system behavior only at critical states against the appropriate set of properties associated with a given state. The paper discusses which 'gedanken' experiments can be considered for this purpose and how they can be derived for various specifications of communication protocols. The results presented in the paper are followed by an illustrative protocol example that demonstrates the efficiency of the proposed approach.

Ensuring the consistency of protocol implementation and protocol specification is the basic premise of wireless communication. With the application of wireless communication in more and more fields, the wireless communication environment becomes more and more complex, and the fault coverage of wireless protocol conformance testing is also facing more and more challenges. To solve this problem, this paper uses Finite State Machine (FSM) as a formal description tool for wireless protocols and presents a combining test method based on two test methods with complementary characteristics in the test technologies based on structural coverage and state identification. Then, the paper evaluates the effectiveness of the method based on 14 empirical cases. The experimental results show that the fault coverage of each empirical case can be effectively improved to 100% when the average test cost is only increased by 17.99%.

Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by introducing a massively parallel MapReduce version of the well-known Exact Algorithm. To the best of our knowledge, this is the first study to tackle this task using the MapReduce approach. First, we give a concise overview of the well-known Exact Algorithm for deriving distinguishing sequences from nondeterministic finite state machines. Second, we propose a parallel algorithm for this problem using the MapReduce approach and analyze its communication cost using Afrati et al. model. Furthermore, we conduct a variety of intensive and comparative experiments on a wide range of finite state machine classes to demonstrate that our proposed solution is efficient and scalable.

Existing stock price models are based on time series methodologies which are hard to estimate and involves lots of assumptions. This study, in contrast, assumes that the stock prices follow stochastic process that possesses Markov dependency with finite state transition probabilities. For this purpose, daily stock index data from Pakistan Stock Exchange (PSE) is collected from 2010-2015 and categorized in to 10 state spaces. Based on the results of state transition model, the study highlights the most probable state of return and also its transition into another state. Further, the study used Monte Carlo method of stock index simulations both Markov chain and original stock index. The analysis shows that it is possible to model and forecast stock index by capturing Markov process. The results of the study are helpful for investors in selecting right time of making investment and for academician to think about more sophisticated methods of state identification.

Efficient checking sequences for testing finite state machines

This paper proposes a framework for analyzing video of physical processes as a paradigm of dynamic data-driven application systems (DDDAS). The algorithms were tested on a combustion system under fuel lean and ultra-lean conditions. The main challenge here is to develop feature extraction and information compression algorithms with low computational complexity such that they can be applied to real-time analysis of video captured by a high-speed camera. In the proposed method, image frames of the video is compressed into a sequence of image features. Then, these image features are mapped to a sequence of symbols by partitioning of the feature space. Finally, a special class of probabilistic finite state automata (PFSA), called D-Markov machines, are constructed from the symbol strings to extract pertinent features representing the embedded dynamic characteristics of the physical process. This paper compares the performance and efficiency of three image feature extraction algorithms: Histogram of Oriented Gradients, Gabor Wavelets, and Fractal Dimension. The k-means clustering algorithm has been used for feature space partitioning. The proposed algorithm has been validated on experimental data in a laboratory environment combustor with a single fuel-injector.