Path Coverage Criteria for Palladio Performance Models

Model-based testing (MBT) studies how test cases are generated from a model of the system under test (SUT). Many MBT methods rely on building an automaton from the model and then they generate test cases by covering the automaton with different path coverage criteria. However, if a model of the SUT is a logical formula over some complex mathematical theories (such as set theory) it may be more natural or intuitive to apply coverage criteria directly over the formula. On the other hand, domain partition, i.e. the partition of the input domain of model operations, is one of the main techniques in MBT. Partitioning is conducted by applying different rules or heuristics. Engineers may find it difficult to decide what, where and how these rules should be applied. In this paper we propose a set of coverage criteria based on domain partition for set-based specifications. We call them testing strategies. Testing strategies play a similar role to path- or data-based coverage criteria in structural testing. Furthermore, we show a partial order of testing strategies as is done in structural testing. We also describe an implementation of testing strategies for the Test Template Framework, which is a MBT method for the Z notation; and a scripting language that allows users to implement testing strategies.

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Basis Path Coverage Criteria for Smart Contract Application Testing

2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) ◽

10.1109/cyberc.2019.00016 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xinming Wang ◽

Zhijian Xie ◽

Jiahao He ◽

Gansen Zhao ◽

Ruihua Nie

Keyword(s):

Coverage Criteria ◽

Smart Contract ◽

Path Coverage

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Using predictive models to evaluate the quality of a test suite at class and method level.

10.5753/cbsoft_estendido.2020.14613 ◽

2020 ◽

Author(s):

Keslley Lima Silva ◽

Érika Cota

Keyword(s):

Predictive Models ◽

Continuous Process ◽

Machine Learning Algorithms ◽

Test Suite ◽

Software Systems ◽

Test Case ◽

Test Coverage ◽

Coverage Criteria ◽

Class Level ◽

Path Coverage

Testing is an indispensable part of the software development process and is a continuous process during the development life cycle. In this context, examining the behavior of software systems to reveal potential problems is a crucial task. To this end, the test suites usually are utilized to examine the software quality. However, test suite quality control is hard for the tester, especially in an evolving system. Such control is needed to assure and improve the test suite's quality and the application as a consequence. Currently, test coverage criteria are used as a mechanism to assist the tester in analyzing the test suite (e.g., find the weaknesses, and add a new test case or test inputs). However, more strong coverage criteria (potentially showing less glaring weaknesses) are challenging to assess. In this work, we propose a different approach to support the developer in evaluating the test suite quality based on more powerful test coverage criteria. We will follow the Knowledge Discovery in Database process using machine learning algorithms to estimate the prime path coverage at the method and class level. For this purpose, we will create two large datasets consisting of source code metrics and test case metrics from 12 open-source Java projects, and these datasets will be used in the training process to build the predictive models. Using the built models, we expected to predict the prime path coverage at the method and class level with a reliable prediction performance.

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