Zuohua Ding

Modeling Self-Adaptive Software Systems With Learning Petri Nets

Traditional models unable to model adaptive software systems since they deal with fixed requirements only, but cannot handle the behaviors that change at runtime in response to environmental changes. In this paper, an adaptive Petri net (APN) is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) it can model a runtime environment; 2) the components in the model can collaborate to make adaption decisions while the system is running; and 3) the computation is done at the local component, while the adaption is for the whole system. We illustrate the proposed APN by modeling a manufacturing system.

Testing Central Processing Unit scheduling algorithms using Metamorphic Testing

Central Processing Unit (CPU) scheduling is used to allocate CPU for multiple processes. CPU is one of the most important resources in the computer system, and its scheduling is vital and influential in operating systems. Thus, it is necessary to ensure the correctness of the CPU scheduling program. However, testing the correctness of a scheduling program is difficult because it is hard to verify the correctness of its output, which is known as the test oracle problem in software testing. Metamorphic Testing (MT) which has been recently proposed to alleviate the test oracle problem, is applied to test the CPU scheduling program. In this paper, we use MT to test the Highest Response Ratio Next (HRRN) scheduling algorithm. Two simulators of HRRN scheduler are used in the evaluation of our method. Surprisingly, some real life faults in one open source simulator are detected by MT. Further experiments are performed based on mutants, and the experimental results show that MT is an effective strategy to test CPU scheduler.

A New Class of Petri Nets for Modeling and Property Verification of Switched Stochastic Systems

Switched stochastic systems (SSS) can be used to describe hybrid systems with randomness. However, the languages to describe their discrete switching logic and stochastic dynamic processes are different, and this difference makes their design and analysis hard. This paper proposes a new Petri net model, namely stochastic-differential Petri net (S-DPN), to describe both discrete switching logic, represented by a Markov chain, and stochastic dynamic processes, represented by a set of stochastic differential equations. We then apply a model checking technique to S-DPN to check the correctness of the requirements of SSS. A temperature control system is used to demonstrate the effectiveness of our method.

Modeling self-adaptive software systems with learning petri nets

Traditional models unable to model adaptive software systems since they deal with fixed requirements only, but cannot handle the behaviors that change at runtime in response to environmental changes. In this paper, an adaptive Petri net (APN) is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) it can model a runtime environment; 2) the components in the model can collaborate to make adaption decisions while the system is running; and 3) the computation is done at the local component, while the adaption is for the whole system. We illustrate the proposed APN by modeling a manufacturing system.

A software cybernetics approach to self-tuning performance of on-line transaction processing systems

We construct two layer fuzzy rules based on the collected data.We develop algorithms from software cybernetics view to reconfigure the performance control parameters.We show self-tuning performance for On-Line Transaction Processing. Self-tuning performance of On-Line Transaction Processing (OLTP) Systems is a challenging and time-consuming task since multiple performance parameters are needed to be automatically configured in Database Management Systems (DBMSs). In this paper, we present a software cybernetics approach to self-tune the performance of DBMSs. A DBMS is designed with an adaptive control based on fuzzy logic such that it has the capability to control objects, i.e., the performance parameters, and update the controller itself, i.e., a set of fuzzy rules in our case. The principles and concepts in software cybernetics are applied to guide the synthesis of software controllers for monitoring and adapting system behaviors. Experimental results for On-Line Transaction Processing using TPC-C, a benchmark of the Transaction Processing Performance Council, show that the proposed method is feasible and effective.

A metamorphic testing approach for supporting program repair without the need for a test oracle

Test suite based automated program repair (APR) relies on a test oracle to determine the execution result of individual test cases. The applicability of APR techniques, therefore, is limited by the fact that test oracles may not exist. Metamorphic Testing (MT) is a testing approach that, rather than checking the correctness of individual test outputs, checks testing results through verification of relations among multiple test cases and their outputs: MT can therefore be applied without test oracles. This paper presents an integration of MT with APR that enables application of APR without the need for a test oracle. Two important issues for this integration which have been thoroughly investigated and addressed are: (1) feasibility — which is addressed by proposing a framework to support the integration, and then presenting MT-GenProg, a tool incorporating MT with the popular APR technique GenProg; and (2) effectiveness — which is confirmed through an empirical study of GenProg and MT-GenProg on 1,143 program versions from the IntroClass benchmark suite, demonstrating MT-GenProg’s comparable performance to GenProg, in terms of repair effectiveness. We conclude that the proposed integration is both practically feasible and effective, and thus successfully extends APR techniques to a broader application domain.

Petri net based test case generation for evolved specification

Model-based testing can use a model to test a concrete program’s implementation. When the model is changed due to the evolution of the specification, it is important to maintain the test suites up to date, such that it can be used for regression testing. A complete regeneration of the whole test suite from the new model, although inefficient, is still frequently used in practice. To address this problem effectively, we propose a test case reusability analysis technique to identify reusable test cases of the original test suite based on graph analysis, such that we can generate new test cases to cover only the change-related parts of the new model. The Market Information System (MIS) is employed to demonstrate the feasibility and effectiveness of the proposed method. Our experimental results show that the use of our method saves about 31.5% test case generation cost.

Generating Petri Net-Based Behavioral Models From Textual Use Cases and Application in Railway Networks

A software systems requirements are often specified by textual use cases due to the latters concrete and narrative style of expressions. However, they have limitation in the synthesis of the system behavior since they have a poor basis for the formal interpretation. Existing synthesis techniques are either largely manual or focus on the use case interactions. We present a framework from a model-based point of view to automatically synthesize system behavior from textual use cases to a Petri net model. The generated net model can well describe component module interactions and thus can be used to check the requirement properties. The function of Send-Railway-Emergency-Call of European Integrated Railway Radio Enhanced Network is used to show the proposed method. Moreover, the experimental results on a set of examples demonstrate the effectiveness of the proposed method.

Modeling Self-Adaptive Software Systems by Fuzzy Rules and Petri Nets

A self-adaptive software system is one that can autonomously modify its behavior at runtime in response to changes in the system and its environment. It is a challenge to model such a kind of systems since it is hard to predict runtime environmental changes at the design phase. In this paper, a formal model called intelligent Petri net (I-PN) is proposed to model a self-adaptive software system. I-PN is formed by incorporating fuzzy rules to a regular Petri net. The proposed net has the following advantages. 1) Since fuzzy rules can express the behavior of a system in an interpretable way and their variables can be reconfigured by the runtime data, the proposed model can model runtime environment and system behavior. 2) Since a fuzzy inference system with well-defined semantics can be used in a complementary way with other model languages for the analysis, thus the proposed model can be analyzed, even though it is described in two different languages: component behaviors in Petri nets while logic control in fuzzy rules. 3) The proposed model has self-adaption ability and can make adaptive decisions at runtime with the help of fuzzy inference reasoning. We adopt a manufacturing system to show the feasibility of the proposed model.

A Real-Time and Fully Distributed Approach to Motion Planning for Multirobot Systems

Motion planning is one of the most critical problems in multirobot systems. The basic target is to generate a collision-free trajectory for each robot from its initial position to the target position. In this paper, we study the trajectory planning for the multirobot systems operating in unstructured and changing environments. Each robot is equipped with some sensors of limited sensing ranges. We propose a fully distributed approach to planning trajectories for such systems. It combines the model predictive control (MPC) strategy and the incremental sequential convex programming (iSCP) method. The MPC framework is applied to detect the local running environment real-timely with the concept of receding horizon. For each robot, a nonlinear programming is built in its current prediction horizon. To construct its own optimization problem, a robot first needs to communicate with its neighbors to retrieve their current states. Then, the robot predicts the neighbors’ future positions in the current horizon and constructs the problem without waiting for the prediction information from its neighbors. At last, each robot solves its problem independently via the iSCP method such that the robot can move autonomously. The proposed method is polynomial in its computational complexity.