Metamorphic Testing for Cybersecurity.

Testing is a major approach for the detection of software defects, including vulnerabilities in security features. This article introduces metamorphic testing (MT), a relatively new testing method, and discusses how the new perspective of MT can help to conduct negative testing as well as to alleviate the oracle problem in the testing of security-related functionality and behavior. As demonstrated by the effectiveness of MT in detecting previously unknown bugs in real-world critical applications such as compilers and code obfuscators, we conclude that software testing of security-related features should be conducted from diverse perspectives in order to achieve greater cybersecurity.

How to test bioinformatics software?

Bioinformatics is the application of computational, mathematical and statistical techniques to solve problems in biology and medicine. Bioinformatics programs developed for computational simulation and large-scale data analysis are widely used in almost all areas of biophysics. The appropriate choice of algorithms and correct implementation of these algorithms are critical for obtaining reliable computational results.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing.

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no "test oracle" to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications.

Application of Metamorphic Testing to Supervised Classifiers.

Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no "test oracle" to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends.

An innovative approach for testing bioinformatics programs using metamorphic testing.

Background: Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguided downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs.