The network generally consists of a ring of substations (converting medium to low voltage), from where distribution feeders originate in a radial topology towards multiple households, each with a smart meter installed. The scheduler combining Earliest Due Date First and Shortest Processing Time First prioritization with Maximum Granularity Allocation subcarrier allocation achieves the highest reliability for nearly all outage percentages. We see a performance degradation as the granularity of the UL subcarrier allocation decreases (from Maximum Granularity Allocation to Least Granularity Allocation).
The study concluded that 20 dB coverage enhancement can be achieved using the identified techniques. On the other hand, in condition coverage, all possible outcomes of all conditions must be tested at least once. In the MCC coverage metric, all statements must be executed and all combinations of truth values in each decision must occur at least once to reach full coverage. The coverage of a program is the number of executed statement blocks and condition combinations divided by their total number in the program. Because compound conditions list all possible
Code coverage
cases, you must find the two cases that can result in changes to
the entire expression. The modified condition is covered only if
What is Condition Coverage Testing?
the two compound conditions are covered.
In the not-so-distant past, code coverage tools were specialist items provided by third-party EDA vendors. However, this capability is now considered important enough that all of the big boys have code coverage integrated into their verification (simulation) environments, but, of course, the feature sets vary among offerings. As can be seen, the CC1 blocks are mapped to two TDMA frames, whereas in case of CC4 32 blind transmissions are used, spread over four 51 multiframes, to reach devices in extreme coverage conditions.
1.2.2 Coverage enhancement
In this section, we provide a look at the nature of such models and the implications of using system models or test models for test generation. Since the topic of model-based testing is very similar to the domain of model-driven development (MDD) and model-driven architecture (MDA) [51], one could assume that models created by developers are mostly reused for testing purposes. Unfortunately, since MBT often requires a separate model for testing purposes, the reuse of models created by developers constitutes another challenge. A testing strategy based around deliberately introducing faults into a system and then determining the effectiveness of test data by measuring how many of these faults it detects.
These are mostly based on former publications [26,40,69,47] and related literature. A particular focus was given to Smart Grids application, which represent one of the target use cases that steered most the NB-IoT development. Many candidates are rejected or down-leveled due to poor performance in their System Design Interview. Stand out in System Design Interviews and get hired in 2023 with this popular free course.
In software engineering, code coverage is a percentage measure of the degree to which the source code of a program is executed when a particular test suite is run. Some of the most basic are the percentage of program subroutines and the percentage of program statements called during execution of the test suite. 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.
For instance, path coverage implies decision, statement and entry/exit coverage. Decision coverage implies statement coverage, because every statement is part of a branch. Several tool vendors for model-based testing as Conformiq,7 sepp.med8 or Smartesting9 state that they use system models or test models for test generation, respectively. In several cases, the vendors seem to be proud to support test generation from one of them, e.g., from system models instead of test models. When it comes to using complex system models with a potentially infinite number of behaviors compared to models of single tests, this can be understood easily.
Thereby, the main target should be the understanding and the validation of the software requirements in early stages of software development. While some scenarios propose sharing models (one model for test team and development team), some scenarios require separated models (one model for each test and development team, respectively). Using shared models can support close collaboration, face-to-face conversation, and simplicity. However, if the same models are used for development and testing, specification errors cannot be found [58]. Using separate models makes the teams for development and test more independent and enables finding specification errors, thus assuring working software.
Only a few investigate non-functional testing such as performance testing, load testing, and usability testing. MISTA [33] is an open-source tool that generates test cases from models of finite-state machines or function nets. The formats of the test cases cover several languages (Java, C, C++, C#, PHP, Python, HTML, and VB) and test frameworks (xUnit, Selenium IDE, and Robot framework).
A digest of the tools is presented in Table 1 and provides a comparison of the different MBT tools based on the testing type, test selection, and test generation technology. It also gives an indication of the test generation process (online, offline, or both). As covering array strength increased, the difference between minimum and maximum code coverage became narrower; thus, higher strength test arrays produced better coverage https://www.globalcloudteam.com/ and were also more stable in the level of coverage produced. Statement coverage is the proportion of source statements exercised by the test set. Statement coverage is a relatively weak criterion, but provides a level of confidence that some basic testing has been done. When using the White Box method of testing called Multiple Condition Coverage, do we take all conditional statements or just the ones with multiple conditions?
Since test cases are the most important artifact in software testing, also the editing of such based on test models is important. Test case editors are based on a so-called test specification model, which describes the abstract structure of test cases. Based on this definition, concrete test cases for manual or automated execution can be generated. Finally, the test data used during test cases can be also generated automatically based on models by using test data generators.
Models having a well-defined syntax and semantics can be handled by computers, which obviously bring efficiency into the test process. The state of the art in modeling techniques supports in creating good design. Depending on the context of development, formal or semi-formal notations can be used.
Assume this function is a part of some bigger program and this program was run with some test suite. These examples show that even if MBT has reached a mature degree and has proved its industrial applicability, there are still remaining future challenges. Especially, the adaptability and integrability of MBT into new software engineering methodologies is very important. In this section we address some challenges and possible future directions in MBT.
- Surveys on quality assurance show that most of the errors in software are introduced during requirements elicitation within the development process [35].
- In contrast, one of our findings in [71] is that test models can also be used for implementation.
- Due to changeable coverage criteria and automated test case generation, the test team can conduct different testing scenarios and gain experience for further development cycles and projects.
- The test cases are in stored XML and can be transformed to JUnit test cases via an integrated convector.
- An XML document defining the SUT as a set of functions is required as input as well as the data space for variables of the functions.
This iterative process is consistent with the traditional use of the MCDC criterion as a check on test adequacy, as described previously. The integrated use of covering array-based tests, with a model checker to determine expected results for each test, was found to be extremely successful in reducing testing costs for MCDC. This tool takes a function block diagram (FBD) as an input model and integrates the UPPAAL [23] model checker to perform symbolic reachability analysis on FBD models for test case generation. A set of coverage criteria, including decision coverage and condition coverage, are used to guide the generation process. This tool presents a simulation environment to simulate the abstract test cases against the FBD models, and also a search-based algorithm to generate executable test cases in C. Section 4.2 presents EC-PDTCH MAC-layer data rates in the range of 0.5–0.6 kbps and 0.5–2.3 kbps in the uplink and downlink, respectively.
These surveys show also that most of the errors are detected first during the testing that comes very late in the development process. Other surveys show that the later the errors are detected the more expensive is the correction of the errors [63]. Thus, the quality of requirements has an impact on the quality of the software product. Requirements engineering (RE) aims at capturing high quality requirements and maintaining them. We believe that MBT techniques and RE techniques can extend each other and be combined in order to give a fully fledged quality assurance suite.
