Metamorphic testing is a technique that uses metamorphic relations (i.e., necessary properties of the software under test), to construct new test cases (i.e., follow-up test cases), from existing test cases (i.e., source test cases). Metamorphic testing allows for the verification of testing results without the need of test oracles (a mechanism to detect the correctness of the outcomes of a program), and it has been widely used in many application domains to detect real-world faults. Numerous investigations have been conducted to further improve the effectiveness of metamorphic testing. Recent studies have emerged suggesting a new research direction on the generation and selection of source test cases that are effective in fault detection. Herein, we present two important findings: i) a mutant reduction strategy that is applied to increase the testing efficiency of source test cases, and ii) a test suite minimization technique to help reduce the testing costs without trading off fault-finding effectiveness. To validate our results, an empirical study was conducted to demonstrate the increase in efficiency and fault-finding effectiveness of source test cases. The results from the experiment provide evidence to support our claims.

Automated test case generation has helped to reduce the cost of testing. However, developing effective test oracles for these automatically generated test cases still remains a challenge. Metamorphic testing (MT) has become a well-known software testing approach over the years. This testing technique can effectively alleviate the oracle problem faced when testing using metamorphic relations (MRs) to determine whether a test case is passed or failed. In this work, we conduct an empirical study on an open source linear algebra library to evaluate whether MRs can be utilized to improve the fault detection effectiveness of automatically generated test cases. Our experiment suggests that MRs can help to improve the fault detection effectiveness of automatically generated test cases.