# Code-Aware Combinatorial Interaction Testing

## TL;DR

This paper presents a novel gray-box combinatorial interaction testing approach that leverages internal code structure to improve fault detection over traditional black-box methods.

## Contribution

It introduces a new method for CIT that considers input parameter impact based on internal code analysis, enhancing fault detection capabilities.

## Key findings

- Improved fault detection compared to equal impact parameter approach
- Applied to five case studies with positive results
- Demonstrated effectiveness in real-world scenarios

## Abstract

Combinatorial interaction testing (CIT) is a useful testing technique to address the interaction of input parameters in software systems. In many applications, the technique has been used as a systematic sampling technique to sample the enormous possibilities of test cases. In the last decade, most of the research activities focused on the generation of CIT test suites as it is a computationally complex problem. Although promising, less effort has been paid for the application of CIT. In general, to apply the CIT, practitioners must identify the input parameters for the Software-under-test (SUT), feed these parameters to the CIT tool to generate the test suite, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black-box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper introduces a different approach to use the CIT as a gray-box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. We applied our approach to five reliable case studies. The results showed that this approach would help to detect new faults as compared to the equal impact parameter approach.