Use this type of coverage to determine whether every statement in the program has been

invoked at least once. The Function metric records whether or not each function in a program has been

Model-Based Testing: Achievements and Future Challenges

called at least once. One target per function is created and a target is covered

the first time the function is called. Needs to expire before the network can send the next EC-PACCH/D containing an Ack/Nack report as well as a new FUA. Just as for the downlink, this implies that eight MCS-9 blocks can be transmitted every 100ms.

Multiple Condition Coverage (MCC)

Besides the advantages and requirements, model-based testing currently faces several challenges. First, the automatic generation, which can be driven by coverage criteria can lead to the test case explosion problem. The number of test cases generated from a test model can be infinite or not multiple condition coverage practicable. This can result from mistakes made during the modeling process or from inadequately chosen coverage criteria. The symbolic execution algorithm [25] is used by DIVERSITY to use symbolic values for inputs rather than actual inputs to generate multiple test cases consecutively.
This technique requires the coverage of all conditions that can affect or determine the decision outcome. Condition Coverage or expression coverage is a testing method used to test and evaluate the variables or sub-expressions in the conditional statement. The goal of condition coverage is to check individual outcomes for each logical condition. Condition coverage offers better sensitivity to the control flow than decision coverage. Unlike black box testing, which focuses on ensuring a smooth user experience, white box testing is intensive.
For example, the code in a particular file
might be already be covered by a different unit test, making it undesirable

to include that code when measuring the coverage of subsequent tests. In order to suffice valid condition coverage for this pseudo-code following tests will be sufficient. Condition coverage is seen for Boolean expression, condition coverage ensures whether all the Boolean expressions have been evaluated to both TRUE and FALSE. In [NLZ18] various scheduling designs are compared with the aim of maximizing the transmission reliability.
The test cases are in stored XML and can be transformed to JUnit test cases via an integrated convector. PragmaDev Studio [37] is a commercial tool with complete support for all the MBT steps. This toolset allows users to create the MBT models in SDL and correspondingly generates the test cases in TTCN-3. PragmaDev Studio integrates with the core of DIVERSITY and uses the symbolic execution algorithm for test case generation and the MBT model validation. PragmaDev Studio has published a free version for users with small MBT projects. Path coverage does not subsume multiple condition coverage because you
can execute all the paths without exercising all the conditions.

In addition, CertifyIt can publish the test cases in script format to facilitate test execution, and the traceability is also well maintained for results analysis. MoMuT is a set of model-based test case generation tools that work with the UML state machine, timed automata, requirement interfaces, and action systems [35]. A fault localization mechanism is included in MoMuT for debugging purposes when a test case fails. Modbat [34] is an open-source tool based on extended finite-state machines specialized for testing the APIs of software. FMBT [26] is an open-source tool developed by Intel that generates test cases from models written in the AAL/Python pre/postcondition language. It provides the necessary interfaces to test a wide range of objects from individual C++ classes to GUI applications and distributed systems containing different devices.
The last point noted above may also explain the significant difference in coverage success shown in a different study that investigated the effectiveness of CT for achieving MCDC coverage. Bartholomew [95,96] applied combinatorial methods in producing MCDC-adequate test suites for a component of software defined radio system, showing that tests based on covering arrays could produce 100% MCDC coverage. Recall that MCDC subsumes branch coverage, which in turn subsumes statement coverage, so full MCDC coverage means that statement and branch coverage were 100% as well. A key feature in the application of MCDC is that tests are constructed based on requirements. In conclusion, code coverage testing is a dynamic process that covers multiple aspects of a code base to ensure high-quality and reliable software. Each of the code coverage metrics brings a unique perspective into the code, each with its own strengths.

A data coverage measure based on star discrepancy [29] is used to guide the test generation and ensure the test cases are relatively equally distributed over the possible data space. Statement coverage is the proportion of source statements exercised by the test set. The EC-PDTCH/U peak physical layer data rate matches the EC-PDTCH/D 489.6 kbps across the 20 ms TTI. For devices only supporting GMSK modulation on the transmitter side, the highest modulation and coding scheme is MCS-4, which contains a RLC/MAC header of 4 octets and a single RLC block of 44 octets.
The goal of decision coverage testing is to cover and validate all the accessible source code by checking and ensuring that each branch of every possible decision point is executed at least once. In this study, a module of 579 lines was instrumented for branch and condition coverage and then tested with the objective of achieving MCDC requirements specified by the Federal Aviation Administration. Initial tests obtained results similar to those in Ref. [49], with approximately 75% statement coverage, 71% branch coverage, and 68% MCDC coverage. However, full branch coverage, and therefore statement coverage also, was obtained after “a brief period of iterative test case generation” [95], which required about 4 h. In a few cases, obtaining complete MCDC coverage required construction of code stubs to force a particular sequence of tests, with specific combinations, to be executed.

• Achieving structural coverage is viewed as a check that the test set is adequate, i.e., the MCDC source coverage is not the goal in itself, only a metric for evaluating the adequacy of the test set.
• Reactis for C can disable coverage for all targets within a selected file or library.
• Black box testing is a software testing methodology in which the tester analyzes the functionality of an application without a thorough knowledge of its internal design.
• The last point noted above may also explain the significant difference in coverage success shown in a different study that investigated the effectiveness of CT for achieving MCDC coverage.
• Making statements based on opinion; back them up with references or personal experience.

Based on the input to the program, some of the code statements may not be executed. The goal of Statement coverage is to cover all the possible path’s, line, and statement in the code. In black box testing, the testing team analyzes the workings of an application without first having an extensive understanding of its internal structure and design. Due to its nature, black box testing is sometimes called specification-based testing, closed box testing, or opaque box testing. Reactis for C uses a number of different coverage metrics

to measure how thoroughly a test or set of tests exercises a program.