Coverage Criteria for State-Based Testing

State-based testing (SBT) is known as deriving test cases from state machines and examining the dynamic behaviour of the system. It helps to identify various types of state-based faults within a system under test (SUT). For SBT, test cases are generated from state chart diagrams based on various coverage criteria such as All Transition, Round Trip Path, All Transition Pair, All Transition Pair with length 2, All Transition Pair with length 3, All Transition Pair of length 4 and Full Predicate. This article discuses a number of coverage criteria at the design level to find out various types of state-based faults in SBT. First, the intermediate graph is generated from a state chart diagram using an XML parser. The graph is traversed based on the given coverage criteria to generate a sequence of test cases. Then, mutation testing and sneak-path testing are applied on the generated test cases to check the effectiveness of the generated test suite. These two are common methods for checking the effectiveness of test cases. Mutation testing helps in the number of seeded errors covered whereas sneak-path testing basically helps to examine the unspecified behavior of the system. In round trip path (RTP), it is not possible to cover all paths. All transition is not an adequate level of fault detection with more execution time compared to all transition pair (ATP) with length 4 (LN4). In the discussion, ATP with LN4 is the best among all coverage criteria. SBT can able to detect various state-based faults-incorrect transition, missing transition, missing or incorrect event, missing or incorrect action, extra missing or corrupt state, which are difficult to detect in code-based testing. Most of these state-based faults can be avoided, if the testing is conducted at the early phase of design.

Utilization Patterns of Disease-Modifying Antirheumatic Drugs for the Treatment of Rheumatoid Arthritis: Rationale for Improving the Harmonization of Coverage Criteria

Utilization patterns of csDMARDs were highly comparable between drug plans overall (in decreasing order: methotrexate, hydroxychloroquine, sulfasalazine, leflunomide, and azathioprine). The proportion of csDMARDs were comparable (e.g., approximately 30% of csDMARD use for methotrexate), although differences in coverage criteria may have resulted in variances in the use of leflunomide. Differences in adjudication of coverage criteria may have resulted in a modest variance in the number of csDMARDs used prior to initiating bDMARDs (i.e., allowing for an “early escape” to bDMARDs for some jurisdictions such as Manitoba and the Atlantic provinces). The mean time to initiate bDMARD therapy (range of 664 to 792 days) revealed a divergence between jurisdictions into 2 groupings whereby Manitoba, Saskatchewan, and the Atlantic provinces drug plans (mean time of 664 to 681 days) saw the initiation of bDMARDs approximately 4 months faster versus other jurisdictions (British Columbia, Alberta, and Ontario, with a mean time of 748 to 792 days), possibly due to their coverage criteria not requiring 3 lines of csDMARDs therapy. Despite differences in the time to initiate bDMARDs, there was no notable difference in the persistence of bDMARDs 6 months after the initiation for any drug plan (61% to 76% range for patients 67 years of age and older). Utilization patterns of bDMARDs was highly comparable between drug plans (i.e., highest use with adalimumab, etanercept, and infliximab), although British Columbia and Manitoba were the only jurisdictions that saw decreasing costs per patient of bDMARDs over time, likely due to a higher uptake of biosimilars or other managed formulary strategies such as tiering.

Multiple-Searching Genetic Algorithm for Whole Test Suites

A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

From Lustre to Simulink

Model-based design is now unavoidable when building embedded systems and, more specifically, controllers. Among the available model languages, the synchronous dataflow paradigm, as implemented in languages such as MATLAB Simulink or ANSYS SCADE, has become predominant in critical embedded system industries. Both of these frameworks are used to design the controller itself but also provide code generation means, enabling faster deployment to target and easier V&V activities performed earlier in the design process, at the model level. Synchronous models also ease the definition of formal specification through the use of synchronous observers, attaching requirements to the model in the very same language, mastered by engineers and tooled with simulation means or code generation. However, few works address the automatic synthesis of MATLAB Simulink annotations from lower-level models or code. This article presents a compilation process from Lustre models to genuine MATLAB Simulink, without the need to rely on external C functions or MATLAB functions. This translation is based on the modular compilation of Lustre to imperative code and preserves the hierarchy of the input Lustre model within the generated Simulink one. We implemented the approach and used it to validate a compilation toolchain, mapping Simulink to Lustre and then C, thanks to equivalence testing and checking. This backward compilation from Lustre to Simulink also provides the ability to produce automatically Simulink components modeling specification, proof arguments, or test cases coverage criteria.

Testing coverage criteria for optimized deep belief network with search and rescue

A new data-driven programming model is defined by the deep learning (DL) that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the data labeling and manual group. Nowadays, a lot of coverage criteria have been developed, but these criteria basically count the neurons' quantity whose activation during the implementation of a DL structure fulfilled certain properties. Also, existing criteria are not adequately fine-grained to capture delicate behaviors. This paper develops an optimized deep belief network (DBN) with a search and rescue (SAR) algorithm for testing coverage criteria. For an optimal selection of DBN structure, the SAR algorithm is introduced. The main objective is to test the DL structure using different criteria to enhance the coverage accuracy. The different coverage criteria such as KMNC, NBC, SNAC, TKNC, and TKNP are used for the testing of DBN. Using the generated test inputs, the criteria is validated and the developed criteria are capable to capture undesired behaviors in the DBN structure. The developed approach is implemented by Python platform using three standard datasets like MNIST, CIFAR-10, and ImageNet. For analysis, the developed approach is compared with the three LeNet models like LeNet-1, LeNet-4 and LeNet-5 for the MNIST dataset, the VGG-16, and ResNet-20 models for the CIFAR-10 dataset, and the VGG-19 and ResNet-50 models for the ImageNet dataset. These models are tested on the four adversarial test input generation approaches like BIM, JSMA, FGSM, and CW, and one DL testing method like DeepGauge to validate the efficiency of the suggested approach. The simulation results proved that the proposed approach obtained high coverage accuracy for each criterion on four adversarial test inputs and one DL testing method as compared to other models.