Automated Test Sequence Optimization Based on the Maze Algorithm and Ant Colony Algorithm

With the rapid development of China train operation and control system, validity and safety of behavioral functions of the system have attracted much attention in the railway domain. In this paper, an automated test sequence optimization method was presented from the system functional requirement specification of the high-speed railway. To overcome the local optimum of traditional ant colony algorithm, the maze algorithm is integrated with the ant colony algorithm to achieve the dynamical learning capacity and improve the adaptation capacity to the complex and changeable environment, and therefore, this algorithm can produce the optimal searching results. Several key railway operation scenarios are selected as the representative functional scenarios and Colored Petri Nets (CPN) is used to model the scenarios. After the CPN model is transformed into the extensible markup language (XML) model, the improved ant colony algorithm is employed to generate the optimal sequences. The shortest searching paths are found and the redundant test sequences are reduced based on the natural law of ants foraging. Finally, the Radio Blocking Center (RBC) test platform is designed and used to validate the optimal sequence. Testing results show that the proposed method is able to optimize the test sequences and improve the test efficiency successfully

Simulation combined model-based testing method for train control systems

A Train Control System (TCS) is utilised to guard the operational safety of the trains in railway systems. Therefore, functional testing is applied to verify consistency between the TCS and specification requirements. Traditional functional testing in TCSs is mainly based on manually designed test cases, which is becoming unsuitable for testing increasingly complex TCSs. Therefore, Model-Based Testing (MBT) methods have been introduced into TCS functional testing, to improve the efficiency and coverage of TCS testing, with application difficulties. To overcome the difficulties of applying MBT methods to test TCSs, the author introduces simulation combined MBT which combines an MBT method with simulation. Modelling method and implementation method for the proposed approach were explained in detail. Two case studies were undertaken to explore the effectiveness of the testing platform developed. The testing results obtained prove that the testing platform can be utilised to implement the functional testing of TCSs. To prove that the MBT platform is effective in detecting errors in the SUT, validation and verification was undertaken, which include validation of specification requirements and verification of the MBT platform. The testing performance is proven to be better than existing MBT methods in terms of coverage and efficiency