## 2 State of the Art
Moreover, in [9], the authors present issues related to designing, deploying, verifying and validating the Protocollo Vitale Standard in accordance with European safety standards for railway systems, based on tools proposed in normative documents on railways. Moreover, certain articles [13; 14] focus on reviewing methods for studying information exchange protocols in terms of the data scope, performance testing, compatibility testing, and business scenario applications in centralized traffic control (CTC) systems of high-speed railways in the People's Republic of China. The authors of [10; 15] proposed a method of hybrid ASF testing that combines black-box and white-box techniques with a method for generating a test case based on two primary formal languages (HCSP and time automation), as well as two popular tools (UPPAAL and CoVer). The study [11] presents the issue of validating the IM dependency module and the FEC external device module. The proposed environment supplements the traditional manual black-box test and performance tests within the software development process. Herein, validation tests are executed by the device manufacturer prior to supplying the equipment and software to a client, while automatic validation employs the manufacturer's scripts that generate software test cases. The testers decide which test cases are to be executed and the environment executes test scripts in a batch mode. All presentedmethodologies and tools focus on reducing the time it takes to implement internal tests since validation activities are critical and costly in terms of time and effort due to the high functional complexity of RTC systems. The authors of [16] presented a novel approach to generating combinatorial test cases. UML sequence diagrams were employed to describe the dynamic behaviour of a software system to detect defects that have occurred due to interactions between manually entered system input parameters. This is a research study aimed at generating various combinatorial test cases and optimizing them with particle swarm optimization and simulated annealing methods. The approach put forward in this paper is different, since the proprietary method presented herein will involve studying the actual system under its application conditions as a whole, using actual excitation signals and actual equipment responses. Diagnosing control devices is crucial to rail traffic security, and this issue has been extensively discussed in the source literature. Several articles therein are based on testing points, crossing warning lights or signalling devices, which are important elements of traffic control infrastructure. The authors of [17], for example, focused on point diagnostics and suggested monitoring point machines by setting current values and analysing them. The theory of fuzzy neural networks was used for analysing and diagnosing failures. Accordingly, a model, based on specified features, selects the type of the occurring failure from a set of six error types defined as output data based on four failures constituting input data. In [18], the authors defined a greater number of errors and failures and, based on the Dempster-Shafer theory of evidence, proposed a universal method for assessing errors at the level of decision making with respect to information synthesis. This resulted in an increased error diagnosis accuracy. A similar issue was addressed in [19], which presents a concept of a simple fault detection system dedicated to monitoring points parameters (temperature and switching resistance value) using an autoregressive model. The study [20] follows up on the issue of point testing but utilizes the SVM (support vector machine) method. Other devices addressed in research articles include railway crossing protection systems. The authors of [21] applied the ARIMA (autoregressive integrated moving average) model and barrier drive hydraulic failure detection. Failures are detected by comparing such signal values as motor current and voltage, hydraulic pressure and barrier position with reference values.