A Novel Approach for Path-Directed Source Test Case Generation and Prioritization in Metamorphic Testing Using Python

Abstract

Metamorphic testing (MT) represents a robust and innovative methodology that adeptly tackles the challenge of the oracle problem. It supplements traditional testing methods by generating a range of distinct and diverse test cases. However, the generation of effective source test cases, along with their prioritization, continues to be an area of active research interest. In response to this demand, We suggest an innovative and all-encompassing method for generating and prioritizing source test cases. It leverages Python's path tracer and constraint solver to obtain program path constraints, empowering the creation of source test cases with extensive coverage of execution paths, thereby substantially enhancing fault detection effectiveness. Moreover, the proposed approach introduces a sophisticated prioritization technique by assigning higher priority to test cases with higher fault detection capability. Through experimental evaluations on four representative programs, the proposed approach demonstrates exceptional performance and outperforms existing techniques. The incorporation of metamorphic relations enables systematic validation of the behavior of mathematical functions, identifying potential deviations or faults that may arise. Additionally, the integration of mutation testing provides a comprehensive assessment of the approach's effectiveness in fault detection and validation of mathematical functions. This research presents a promising and practical solution to the challenges associated with generating and prioritizing source test cases in metamorphic testing, contributing to the improvement of software testing effectiveness and efficiency. By combining various techniques, we aim to improve fault detection capabilities and provide a practical solution for testing software systems, addressing the specific challenges in the realm of scientific software testing.