Test Suite Generation for CTCS-3 Train Control System Based On TAIO and Mutation Theory

It is confirmed that the Chinese Train Control System (CTCS) level 3 system is adopted in newly-built railways where the train speed is expected to exceed 300 kilometers per hour. In order to construct the simulation and testing platform of the CTCS level 3 system, this paper focuses on the research of functional prototype of Radio Block Centre (RBC), including the following three key points. First, we find out functional requirements of RBC through a profound analysis of the CTCS level 3 system requirements. Then, we present the design of safety computer platform for RBC according to the safety requirements of train control equipments. Last, we design and accomplish the software of the RBC functional prototype on the base of the above achievements. Through the practical application in the simulation and testing platform of the newly-built railway, it is proved that the functional prototype accords with the CTCS level 3 system requirements specification and meets the construction requirements of the CTCS level 3 simulation and testing platform.

The Target Speed Monitor (TSM), which performs the safety critical function of the Chinese Train Control System Level 3 (CTCS-3), is characterized by large parameter input field and fault modes triggered by complicated combinations of parameters. Although combinatorial testing has been successfully applying in testing TSM, redundant test cases are generated because of covering the parameter combinations without interaction, which leads to the problem of high test execution costs. In this paper, an automatic reduction methodology based on decision tree equivalence and test suite output labels is presented. With previous combinatorial test suite generation experiment results of CTCS-3 TSM function, after data preprocessing, some data set have been constructed by test suites and corresponding output labels and they can be used to infer decision trees. After that, the reduction algorithm was used to reduce the data set to realize the test suite reduction. Related experimental results show that this method can achieve a reduction rate of up to 90%, and at the same time, the low-level combination coverage and the fault detection capability of reduced test suites are almost the same as that before the reduction.

Service Oriented Architecture (SOA) provides a new mode for the distributed computer simulation which has the characteristic of scalability, reusability and interoperability. In order to meet the needs of reusability and continuous development functions of the train control simulation system, this paper proposes a distributed simulation framework based on SOA; Meanwhile, using the hierarchical structure, the framework is divided to four levels which include simulation management level, simulation application level, simulation basic resource level and simulation middleware level to achieve scalability, reusability and interoperability. SOA and four hierarchical structure together can support for the compatibility and sustainable of train control simulation system. Finally, Chinese train control system level 3 (CTCS-3) is taken as a case, and the availability and scalability of the framework have been analyzed.

The complexity of railway control system makes some requirement deficiencies hard to find, which results in system failures. It is essential to locate those deficiencies using logs recorded during failure events. In this paper, a model checking based failure analysis approach was proposed and applied to a case of abnormal emergency brake. First, a system model describing the system requirement and an event model depicting the logs were constructed. Next the compositional model was verified through model checking in UPPAAL which then produced a counterexample trace that describes the system behaviour in the failure event. By analysing this trace, an inadequacy was found in the requirement and a modification strategy was brought up which was formally verified to be effective.

At present, the method for fault diagnosis and maintenance of the CTCS-3 (Chinese Train Control System Level 3) electronic equipment relies too heavily on expert knowledge. Moreover, the use of historical fault data is not valued. This paper proposes a sustainable fault diagnosis model based on imbalanced text mining. First, to process fault data from the field recorded in natural language, natural language processing technology is used to extract fault feature words. Then, a term frequency-inverse document frequency model is used to transform the fault feature words extracted from the database into vectors. It is worth noting that imbalance in the fault samples affects the accuracy of this sustainable fault diagnosis model. To solve this problem, we use the borderline-synthetic minority over-sampling technique in the step of predicting train fault components, we also use the backpropagation neural network we proposed and the naive Bayesian model which is commonly used as a classification model, to compare the prediction accuracy of these two algorithms. The experimental results perform well, which proves that the fault diagnosis method using the backpropagation neural network can further assist engineers to complete timely repair and maintenance work. The research in this paper has played a very important role in technical support for intelligent train dispatching and command, and will also play a positive role in technical support for the automatic operation of urban rail transit under the prevention and control of the new coronavirus.

A timely and complete test is an important factor to assure the functionality and safety of a railway signal system before it is put into service. With the development of rail transportation in China, the traditional semiautomatic test methods cannot satisfy the timely and complete test requirements any longer. This paper proposes an automated model-based test method. First, colored Petri nets are used as a formal language to describe the system specification. Second, the all paths covered optimal algorithm and the sequence priority selected algorithm are proposed to generate the test cases and sequences automatically. Third, taking a typical radio block center (RBC) handover scenario as an example, the generated test cases and sequences are applied into the RBC functionality test platform. The testing result validated the feasibility and efficiency of the proposed automated test method. Compared with the random-walk-based test sequence generation algorithm, the repeatability rate of the generated test sequences is reduced by 46%. The test sequences can cover all the generated test cases, and the cases can cover all the related criteria in the function requirements specification of the Chinese Train Control System Level 3.

The safe operation of Chinese Train Control System Level 3 (CTCS-3) is of great significance, particularly with respect to the increasing operational speed and expanding railway networks of the Chinese high-speed railway system, which has drawn deep concern from both customers and strategic makers. A hazard and operability (HAZOP) study has been recognized as an effective systematic examination to identify any potential problems existing in various industrial processes or operations. This paper presents a process of applying a HAZOP study to identify the hazards of a CTCS-3 onboard system for the first time, which is composed of two major parts: system models and hazard identification on the examination session. To better reflect the structure and functions of a CTCS-3 onboard system, the following models are developed, i.e., a reference model, a function hierarchical model, a state diagram, and a sequence diagram. To demonstrate the effectiveness of the HAZOP study, hazard identification on the basis of the functions of the CTCS-3 onboard system and a scenario of temporary speed restriction is considered and employed. The results indicate that existing hazards can be dug out at express speed, which allows the relevant actions to be proposed and implemented to prevent the hazards from spreading to a wide range in the whole CTCS-3 onboard system.

Mutation-oriented test data augmentation for GUI software fault localization

The availability of effective test suites is critical for the development and maintenance of reliable software systems. To increase test effectiveness, software developers tend to employ larger and larger test suites. The recent availability of software tools for automatic test generation makes building large test suites affordable, therefore contributing to accelerating this trend. However, large test suites, though more effective, are resources and time consuming and therefore cannot be executed frequently. Reducing them without decreasing code coverage is a needed compromise between efficiency and effectiveness of the test, hence enabling a more regular check of the software under development. We propose a novel approach, namely REDUNET, to reduce a test suite while keeping the same code coverage. We integrate this approach in a complete framework for the automatic generation of efficient and effective test suites, which includes test suite generation, code coverage analysis, and test suite reduction. Our approach formulates the test suite reduction as a set cover problem and applies integer linear programming and a network-based optimisation, which takes advantage of the properties of the control flow graph. We find the optimal set of test cases that keeps the same code coverage in fractions of seconds on real software projects and test suites generated automatically by Randoop. The results on ten real software systems show that the proposed approach finds the optimal minimisation and achieves up to 90% reduction and more than 50% reduction on all systems under analysis. On the largest project our reduction algorithm performs more than three times faster than both integer linear programming alone and the state-of-the-art heuristic Harrold Gupta Soffa.

Safety as the key quality property among RAMS (reliability, availability, maintainability, and safety) demonstrates the most stringent performance in correspondence with the safety requirements and performance standards like EN 50126. Meanwhile, GNSS (Global Navigation Satellite Systems) are penetrating the railway now widely in non-safety related applications as passenger information, fleet management, etc. GNSS also have great potential for safety-related applications in railway such as the train location determination function, which the safety performance needs to be assured through hazard analysis and risk assessment process. The train location determination by satellite-based localization system is elevating the train control to the next level. The European Train Control System (ETCS) has being trying to implementing Level 3, the Chinese Train Control System (CTCS) has been implementing CTCS Level 3 low cost especially for secondary lines, and the U.S. is implementing train control systems under Positive Train Control (PTC) requirements. The train control system needs GNSS to provide more accurate location information of trains, more flexible and condensed trains on tracks with the consistency of still keeping the current safety level or even improve safety. Some researchers are trying to understand the performance of GNSS (GPS / EGNOS / Beidou) for railway applications from the fundamental accuracy level. A satellite-based train localization unit (SaLuT) as the entity to perform the train location determination function is to bring the GNSS accuracy evaluation up to safety integrity according to the safety requirements and standards for risk assessment. One of the key consequential result derived from the train location is the adequate safety margin. The safety margin, which can also be called as “safe braking distance”, is a margin indicated to rail traffic that would allow the train to stop with the application of normal service braking. The safety margin estimation quality and the risk of the safety margin shows the hazard rate for the safety margin estimation function performed by the designed localization unit SaLuT. This paper discusses the safety margin estimation method considering both GNSS accuracy and integrity assessment aspects of SaLuT, in accordance of the settled safety requirements of location determination function. To analyze the hazard of the safety margin estimation, a formal method is applied to model the SaLuT behavior and functions. The formal method based on stochastic Petri net enables the modeling process to include the GNSS receiver collected real data on the test track into it. The safety margin estimation method together with the risk assessment method using the real data can generate quantitative indicators to represent the localization function and safety margin estimation quality. The data used for the analysis is collected in the Qinghai-Tibet railway line from Golmud station to Ganlong station by SaLuT installed on a locomotive along the track. With the stochastic Petri net model and the systematic equation using the real collected data to estimate the safety margin based on the GNSS technologies, the SaLuT can be validated and verified for its hazard rates, which provides information for the safety cases in order to meet the industrial normative requirements.

Guest Editorial: Special Section from the 11th International Conference on Quality Software (QSIC 2011)

Mutation testing is a well known technique for estimating and improving the quality of test suites. Given a test suite T for a system S, mutation testing systematically creates mutants of S and executes T to measure how many mutants T detects. If T does not detect some (non-equivalent) mutants, T can be improved by adding test cases that detect those mutants. Mutants are created by applying mutation operators. Mutation operators are important because they define the characteristics of the system that are tested as well as the characteristics that are improved in the test suite. While mutation operators are well defined for a number of programming paradigms such as sequential or multi-threaded, to the best of our knowledge, mutation operators have not been defined for the actor programming model. In this paper, we define and classify mutation operators that can be used for mutation testing of actor programs.

Context. Manual unit test creation is a cognitively intensive and time-consuming activity, prompting researchers and practitioners to increasingly adopt automated testing tools. Recent advancements in language models have expanded automation possibilities, including unit test generation, yet these models raise substantial sustainability concerns due to their energy consumption compared to conventional, specialized tools. Goal. Our research investigates whether the energy overhead associated with employing a small language model (SLM) for unit test generation is justified compared to a conventional, lightweight testing tool. We compare and analyze the energy consumption incurred during test suite generation, as well as the fault-finding effectiveness of the resulting test suites, for an SLM (Phi-3.1 Mini 128k) and Pynguin, a purpose-built tool for unit test generation. Method.We posed two research questions: (i) What is the difference in energy usage between Phi and Pynguin during the generation of unit test suites for Python programs?; and (ii) To what extent do unit test suites generated by Phi and Pynguin differ in their fault-finding effectiveness? To rigorously address the first research question, we employed Bayesian Data Analysis (BDA). For the second research question, we conducted a complementary empirical analysis using descriptive statistics. Results. Our Bayesian analysis provides robust evidence indicating that Phi consistently consumes significantly more energy than Pynguin during test suite generation. Conclusions. These findings underscore significant sustainability concerns associated with employing even SLMs for routine Software Engineering tasks such as unit test generation. The results challenge the assumption of universal energy efficiency benefits from smaller-scale models and emphasize the necessity for careful energy consumption evaluations in the adoption of automated software testing approaches.

There exists a direct need to automate the process of test suite generation to get the most optimal results as testing accounts for more than 40% of total cost. One method to solve this problem is the use of meta-heuristic algorithms which iteratively improve the test data to reach the most optimized test suites. This study focuses on the performance evaluation of six meta-heuristic algorithms namely: hill-climbing algorithm (HCA), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search algorithm (CS), bat algorithm (BA) and artificial bee colony algorithm (ABC) by using their standard implementation to optimize the path coverage and branch coverage produced by the test data. The goal of the study was to find the best-suited algorithm to narrow down the future research in the field of test automation for path coverage-based optimization approaches. Each algorithm was first implemented to automatically generate test suites based on the program under test. This was followed by the performance evaluation of each algorithm for five programs written in Java. The algorithms were compared using process metrics: average time, best time, worst time and product metrics: path coverage & objective function values of the generated test suites. Results indicated ABC as the best-suited algorithm as it gave the most optimal test suites in reasonable time. BA was found to be the fastest but produced less optimal results. FA was found to be the slowest algorithm, while CS, PSO and HCA performed in between. These results show the relative performance of the six algorithms for this scenario and may be used by the future researchers to narrow down and improve the best performing algorithms for path coverage-based optimization approaches.

The speed and localization information of the high-speed trains are essential and significant for train control systems. The occurrence of faults in SDUs (Speed Distance Units) will affect the accuracy and effectiveness of location reports to ensure the operation safety of high-speed trains. The fault detection and diagnosis method for SDUs are focused in this paper. By using the on-board GPS (Global Positioning System) measurements, we propose a parallel-filtering-based architecture for detecting the faults in odometers that use the pulse-counting information to estimate the speed and travelling distance of the trains. According to the characteristics of different fault modes, including the hard fault, soft fault and implicated fault, a rule-based fault diagnosis approach is proposed to identify the detected fault and take suggested maintenance measures, where a conservative determination strategy for utilizing the results of the two filters is demonstrated to decrease the missed detection probability. The field operation data from the high-speed train is employed to build the simulation scenarios. The simulation results demonstrate the performance of the proposed method and illustrate its potentials in real-time fault diagnosis and maintenance for speed distance units.