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Measuring Effectiveness of Metamorphic Relations for Image Processing Using Mutation Testing.

Fakeeha Jafari¹, Aamer Nadeem¹

¹Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan.

Journal of Imaging

|April 26, 2024

Summary

This summary is machine-generated.

This study introduces new metamorphic relations (MRs) to improve software testing by enhancing fault detection rates for image processing operations. The proposed MRs significantly outperform existing techniques, identifying previously undetected faults.

Keywords:

image processing metamorphic relations metamorphic testing mutation testing

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Area of Science:

Software Engineering
Computer Vision
Image Processing

Background:

Metamorphic testing is crucial for software systems lacking test oracles.
Metamorphic relations (MRs) effectiveness is key but existing evaluation methods are limited.
Current MR evaluation uses insufficient mutation operators, leading to incomplete fault identification.

Purpose of the Study:

To propose six novel metamorphic relations (MRs) for dilation and erosion operations.
To comprehensively evaluate the fault detection rates of these new MRs using mutation testing.
To compare the effectiveness of the proposed MRs against existing techniques.

Main Methods:

Developed six new MRs for dilation and erosion image processing operations.
Employed mutation testing with eight applicable mutation operators to generate mutants.
Ensured exhaustive mutant generation to identify all potential faults.
Evaluated MRs for edge detection, dilation, and erosion operations.

Main Results:

Proposed MRs for edge detection improved fault detection rates by up to 32% (MR1) and 24% (MR4).
MRs for dilation and erosion showed significant improvements, with MR1 up by 39% and MR8 up by 29%.
The new MRs successfully identified faults missed by existing techniques, demonstrating complementary effectiveness.

Conclusions:

The proposed six MRs offer a more comprehensive approach to evaluating software under test.
Enhanced fault detection rates signify improved software quality and reliability in image processing.
The novel MRs effectively complement existing methods, addressing limitations in fault identification.