Muta-Pro: towards the definition of a mutation testing process

In order to solve the current problems( expensive testing cost and low efficiency of test case generation) in mutation test for Simulink models,a mutation testing process and an optimized set of mutation operators were proposed for Simulink models based on program mutation according to the current mutation operators for the Simulink models. Experiments show that this set of mutation operators can effectively reduce the generation number of mutation models without prejudice to the mutation score of testing case set,thus it will effectively save the testing cost. Then a search-based test case generation method for Simulink models mutation testing was described. The test case generation problem was transformed into the objective function minimization problem,and the test cases which can kill the mutation models were ultimately obtained through the optimization of objective function by algorithm of simulated annealing. Finally,the application of a typical case for the method verified the correctness and effectiveness.

A test suite plays a key role in software testing. Mutation testing is a powerful approach to measure the fault-detection ability of a test suite. The mutation testing process requires a large number of mutants to be generated and executed. Hence, mutation testing is also computationally expensive. To solve this problem, predictive mutation testing builds a classification model to predict the test result of each mutant. However, the existing predictive mutation testing methods only can be used to estimate the overall mutation scores of object-oriented programs. To overcome the shortcomings of the existing methods, we propose a new method to directly predict the mutation score for each statement in process-oriented programs. Compared with the existing predictive mutation testing methods, our method uses more dynamic program execution features, which more adequately reflect dynamic dependency relationships among the statements and more accurately reflects information propagation during the execution of test cases. By comparing the prediction effects of logistic regression, artificial neural network, random forest, support vector machine, and symbolic regression, we finally decide to use a single hidden layer feedforward neural network as the predictive model to predict the statement mutation scores. In our two experiments, the mean absolute errors between the statement mutation scores predicted by the neural network and the real statement mutation scores both approximately reach 0.12.

Under the Cyber Army Modeling and Simulation (CyAMS) program a new model has been created to efficiently model various cyber events. The finite state machine model allows for the modeling of applications by creating behaviors that map to properties of real world applications. The finite state machine model is originally implemented by utilizing the ns-3 parallel discrete event simulator and validated using data from an emulation testbed experiment featuring malware applications. Following the completion of the ns-3 validation work, the CyAMS behavioral simulator was developed to allow for larger scale networks to be modeled, while also allowing both the network and applications to be defined using a behavioral network. This allows for the creation of an accurate mapping between both the simulation and the emulation applications. Validation tests are then carried out to determine both the validity of the model and the potential scalability. Finally, a testbed is proposed that will combine behavioral simulation, traditional parallel discrete event simulation, and emulation. The creation of this end-to-end environment for cyber simulation will allow past, present, and potentially future cyber events to be modeled in order to help understand and potentially mitigate future malicious attacks.

The paper presents an inclusion relation which is used to verify the process of controller synthesis based on incompletely specified finite state machine model. State transition graph is adopted to describe the behavior of controller. State transition graph STG_org was abstract behavior description of controller synthesis generated before synthesized and state transition graph STG_ext was reversely extracted from structure realization of controller synthesis. If STG_org contains STG_ext then corresponding process of controller synthesis was correct. The paper gives a formal verification algorithm of controller synthesis with time complexity. In order to further improve time complexity of algorithm, a verifying algorithm with synthetic information guidance was proposed.

Mutation testing from probabilistic and stochastic finite state machines

There are two main ideas in this paper: (a) regional-sector strategy could be created by a ‘bottom-up’ planning process and (b) the foresight methodology (and especially the mix of its methods) are the support tools for the regulation process on the regional level. We base our work on previous experiences and on the results of the research project named ‘Energy Development Strategy for Lower Silesia by Using Foresight Methods’ and carried out by our team from Wroclaw University of Technology (Poland). First of all, we present the idea of regulation in economies and the ‘bottom-up’ approach in programming regional development in the energy sector. The next part of the article concerns the key issues of foresight methodology and the range of its use. The results of our project are presented in the last part of this paper. The main idea is to show how foresight methods and main experts' methods, e.g. the Delphi method, can successfully support a ‘bottom up’ approach.

This research focuses on the Kawn Subscriptions Manager app, which was developed to help Food and Beverage (F&B) businesses manage customer subscriptions. Currently, the Kawn Subscriptions Manager application does not have a standardized test suite that has been validated. Therefore, in this research, Model-Based Testing (MBT) is applied in testing the Kawn Subscriptions Manager application as a testing method to produce a specific and technical test suite. MBT is a testing method based on the behavioral model of the system being tested. Then, to measure the adequacy of test cases generated by MBT, mutation testing is used. The model is created using the GraphWalker tool and testing is done automatically using Selenium and testNG in the Eclipse IDE text editor. From the results of the study, it is known that MBT testing generates test cases from the behavior model of the function being tested. Testing with MBT shows that no failures were found in the tested functions. Then the average mutation score is 72.18% which indicates that the test cases generated by MBT are adequate or in the medium category. However, it is important to note that while MBT was successful in generating test cases that examined the application's behavior, it was found that the method was unable to detect failures caused by mutants that did not significantly affect the application's behavior. This finding suggests that while MBT can be an effective tool for testing key functions, additional methods are needed to identify failures that may result from minor or insignificant changes to the application code.

Integer overflow is a common vulnerability in Ethereum Smart Contracts (ESCs) and often causes huge economic losses. Smart contracts cannot be changed once it is deployed on the blockchain and thus demand further testing. Mutation testing is a fault-based testing method that can effectively improve the sufficiency of a test for smart contracts. However, existing methods cannot efficiently perform mutation testing specifically for integer overflow in ESCs. Therefore, by analyzing integer overflow in ESCs, we propose five special mutation operators to address such vulnerability in terms of detecting sufficiency in ESC testing. An empirical study on 40 open-source ESCs is conducted to evaluate the effectiveness of the proposed mutation operators. Results show that: (1) our proposed mutation operators can reproduce all 179 integer overflow vulnerabilities in 40 smart contracts, and the generated mutants have high compilation pass rate and integer overflow vulnerability generation rate; moreover, (2) the generated mutants can find the shortcomings of existing testing methods for integer overflow vulnerability, thereby providing effective support to improve the sufficiency of the test.

Reflex molecular testing, the process of profiling specimens at diagnosis, is emerging in oncology, allowing for prompt therapy initiation, and potentially improving outcomes. This study evaluates the impact of reflex BRAF testing on treatment timelines and outcomes in melanoma, lung and colorectal cancers at the McGill University Health Center (MUHC). A retrospective chart review was conducted at the MUHC. The study included patients with melanoma, lung, and colorectal cancers who underwent BRAF testing from 2017 to 2022. Data on demographics, cancer type, stage, test ordering specialist, testing method, and treatment outcomes were extracted. Statistical analyses included descriptive and multivariate regression analyses. 518 BRAF molecular tests were performed, with 173 patients meeting the inclusion criteria [median age 72 (IQR 60-80 years), 46.2% female]. Of these, 75.7% had melanoma, 23.7% colorectal cancer and 0.58% lung cancer. Pathologists ordered 71.1% of BRAF tests, primarily relying on immunohistochemistry. By 2022, all BRAF results were available before oncology consultations. Patients with pre-consultation BRAF results were more likely to receive targeted therapy (59.4% vs 36.4%). The availability of BRAF test results was associated with quicker treatment initiation and better alignment of therapy with mutation status. This study underscores the success of the pathology team at MUHC in implementing reflex BRAF mutation testing, enhancing clinical care by ensuring the timely availability of test results. Delays in testing may adversely affect clinical decision-making and therapy selection, highlighting the need for standardized reflex testing protocols across Canada to optimize patient outcomes in melanoma, lung, and colorectal cancers.

Under the Cyber Army Modeling and Simulation (CyAMS) program, a model validation was conducted using data from an emulated network for malware propagation to compare against the CyAMS finite state representation of network nodes and processes using behavioral simulation. During the validation process, the finite state machine model was effective in identifying important caveats in the emulation tests. Once the experimental parameters were correctly defined, the CyAMS model and the emulated networks showed similar outcomes. The simulation in this case utilized at most 3 CPU cores, whereas the emulation approach required roughly 2,000 real computers and 14,800 virtual machines. The results highlight the possibility that simulation methods can be as effective as emulation test beds in selected cases. Further, simulation results can be an effective tool to verify the goals of emulation based experiments in some cases. Results demonstrated that several orders of magnitude of less computing resources are required for a simulation compared to emulation for this particular test case.

In order to solve the problem of fast fault diagnosis in power system, this paper introduces a method of finite state machine model of fault reasoning for distribution system under time sequence constraints. Firstly, the concept of time sequence constraint characteristics of SOE data from the SCADA alarm window is introduced, which is crucial to judge whether the alarm sequence are from an individual fault accident. Then, by fully considers the “protection-circuit breaker” action as well as its logic, the Finite State Machine model with various system fault types was constructed. Finally, to prove the effectiveness of the method, a set of alarm data in real accident which happen in 10 kV distribution systems is applied to test the model. The result shows that the model can quickly analyze SOE data within the time interval in system accident, and effectively help the operation dispatcher to handle accident and defect elimination.

Recently, researchers have been working on cloud data assurance models to ensure that the data is in compliance with the policies. In such a model, the data is placed in the intended data centres without focusing on data residency issues. Thus, in order to solve such issues, we have proposed a framework known as the data resident storage (DRS) which is intended to achieve data residency protection, thus providing data residency as a service (DRaaS). The finite state machine model is developed for the purpose of verifying the methodology in terms of data location and data access. The validation is carried out in the CloudSim toolkit with the help of defined services. The developed test automation framework places the data only in data centres that are in compliance with data residency laws. Finally, this framework can be hosted by the cloud service provider to provide DRaaS to the end users.

While databases store information and the is the place for the gathering and distribution of information, there are inherent difficulties in linking them together. Often proprietary methods are used which compromise compatibility. This paper presents a new design and engineering model using hyperlinks for sophisticated user interaction with relational databases. The finite state machine model well known from static hyper-linked documents is remodelled to be suitable for dynamically generated hyper-links. The actual implementation of such a system is composed of several parts with potentially different techniques. The usability of Java is compared with the HTML approach. A based toolset has been implemented to generate the different applications thereby also fostering unlimited collaboration. Using this technique, two complete real-life applications have been built. The proposed techniques help to implement stable, robust, flexible, scalable and Web compliant applications with complex functionality and to provide new ways to integrate legacy systems.

Autonomous underwater vehicles (AUVs) are an indispensable tool for marine scientists to study the world's oceans. The Slocum glider is a buoyancy driven AUV designed for missions that can last weeks or even months. Although successful, its hardware and layered control architecture is rather limited and difficult to program. Due to limits in its hardware and software infrastructure, the Slocum glider is not able to change its behavior based on sensor readings while underwater. In this paper, we discuss a new programming architecture for AUVs like the Slocum. We present a new model that allows marine scientists to express AUV missions at a higher level of abstraction, leaving low-level software and hardware details to the compiler and runtime system. The Slocum glider is used as an illustration of how our programming architecture can be implemented within an existing system. The Slocum's new framework consists of an event driven, finite state machine model, a corresponding compiler and runtime system, and a hardware platform that interacts with the glider's existing hardware infrastructure. The new programming architecture is able to implement changes in glider behavior in response to sensor readings while submerged. This crucial capability will enable advanced glider behaviors such as underwater communication and swarming. Experimental results based on simulation and actual glider deployments off the coast of New Jersey show the expressiveness and effectiveness of our prototype implementation.

In distributed systems, a group of multiple processes are cooperating with each other by exchanging messages in networks. A process is considered to be a sequence of events in a finite state machine model. Here, it is significant to decide on which event causally precedes another event for a pair of events in a system. In the famous happened-before relation among events, events are ordered on the basis of send-receive semantics of messages exchanged in networks. In this paper, we discuss a peer-to-peer (P2P) model of a publish/subscribe (P2PPS) system where application events are subscribed, published, and notified by peer process (peers). We discuss the causal dependency of application events in terms of subscription, publication, and notification semantics.