Interactions between software components play a major role in the achievement of goals in complex systems, such as platooning System-of-Systems (SoS). A platooning SoS groups vehicles in order to increase their fuel efficiency and alleviates traffic congestion by enabling driving in close proximity using operation protocols. In a platooning SoS, the execution of typical operations, such as Leave or Merge, consists of 20 micro-operations on average. Owing to this overabundance of sub-operations, interaction failures in a specific operation sequence can occur in an SoS execution. Further, analyzing the root cause of such failures is highly time-consuming, due to the density of the constituent interactions. Existing techniques suffer from two limitations: (1) The majority of the root cause analysis techniques are not capable of isolating faulty interaction sequences, because they do not directly utilize interaction data; (2) The majority of the fault diagnosis techniques assume the preexamined fault knowledge base, which needs too high cost due to limited knowledge in an SoS. To effectively analyze interaction failures in an SoS, we propose a pattern-based faulty interaction analysis technique. To this end, an interaction model is first defined for an SoS, followed by the proposal of a suspicious interaction pattern mining algorithm. During the case study using a platooning simulator, the technique automatically abstracts interaction data from logs and extracts faulty interaction patterns, thereby enabling the identification of seven new unreported interaction failure scenarios. The conclusions of this study can enrich the general fault knowledge base for platooning SoS.

Platooning system is a well-known technology for alleviating traffic congestion and increasing fuel efficiency by grouping vehicles. It has the major characteristics of Systems of Systems (SoS), such as uncertainty. Several internal and external factors of uncertainty exist in the platooning system, such as car accidents, network disconnections, and simultaneous requests from other platoons. These factors make it difficult to guarantee that the system operates correctly in unpredictable scenarios and environments. The existing techniques used to verify the platooning system have two limitations: 1) the lack of consideration of uncertainty in scenarios and environments; 2) the application of exhaustive verification techniques which are vulnerable to the state-explosion problem. Thus, we suggest a statistical verification framework for a platooning SoS to address the above two limitations. The proposed framework automatically generates platooning configurations and scenarios with internal and external uncertain factors considered, and bypasses the state-explosion problem using a statistical verification technique. In this study, experimental results showed that the proposed approach generates 50% more valid scenarios than pure random strategy. In addition, we found two types of undiscovered failures and their causes in the VENTOS platooning system. These results indicate that our approaches enable the deep analysis of the platooning management system.

A System-of-Systems (SoS) is a collection of systems, which consists of independent constituent systems to achieve higher-level goals. As an SoS changes constantly due to external and internal factors, dynamic reconfiguration and evolutionary development must be performed effectively. To manage an SoS that has these characteristics, SoS managers and engineers need to model and verify an SoS that accommodates changes. While many researchers have proposed techniques for modeling and verifying an SoS, little attention has been paid to the verification of a continuously changing SoS. Since the size and complexity of SoSs are too great to apply existing verification techniques, an efficient verification method for dynamically changing SoSs is required. If we can detect the change-related goals of an SoS and the corresponding change-affected parts in an SoS model, we can slice the SoS model in our area of concern and verify the SoS model efficiently. This paper proposes SoS GaP slicer, a verification method that considers the dynamic reconfiguration and evolution of an SoS. Our method could save the cost of verifying the whole of an SoS model by an efficient statistical model checking for SoSs. The experimental results show the accuracy and efficiency of the proposed method.

As Function Block Diagram (FBD) programs are used to implement safety-critical systems such as nuclear reactor protection systems, it is crucial to be able to generate effective test cases. The FBD is one of programming languages that are used for programmable logic controllers (PLCs). PLC programs are repeatedly run within a particular scan time for every execution. Among the constituents of FBD programs, function blocks and feedback variables use the inputs and outputs of the previous scan cycle on which to operate. Researchers have recently developed an automated test generation technique that satisfied several structural test coverage criteria for FBD programs using symbolic execution. However, their work could not generate test sequences for consecutive scan cycle but test inputs for one scan cycle. Test sequences are essential for testing FBD programs that have function blocks and feedback variables. This paper extends previous work in the field by generating test sequences for FBD programs with function blocks in a fully automated manner. The key technique involves explicitly unwinding FBD programs and solving test requirements using an SMT solver. We conduct experiments on increasing the coverage of test requirements by unwinding cycles and evaluating the effectiveness of the test set using mutation analysis with several subject programs, including a real-world reactor protection system. The experimental results show that the proposed approach is able to generate effective test sequences for FBD programs.