# Efficient Test Data Generation for MC/DC with OCL and Search

arXiv: https://arxiv.org/abs/2401.03469 · HTML: https://mlsanity.com/paper/2401.03469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 108 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Practical Constraint Solving for Generating System Test Data](https://mlsanity.com/paper/1902.00397/) (2019)
- [Model-based Digital Twins of Medicine Dispensers for Healthcare IoT Applications](https://mlsanity.com/paper/2312.04662/) (2023)
- [CDST: A Toolkit for Testing Cockpit Display Systems of Avionics](https://mlsanity.com/paper/2001.07869/) (2020)
- [Model-based Automated Testing of Mobile Applications: An Industrial Case Study](https://mlsanity.com/paper/2008.08859/) (2020)
- [Model-based testing in practice: An experience report from the web applications domain](https://mlsanity.com/paper/2104.02152/) (2021)

## Built on by (most cited first, 1 of 1)

- [Automated System-level Testing of Unmanned Aerial Systems](https://mlsanity.com/paper/2403.15857/) (2024)

## Abstract (arXiv)

System-level testing of avionics software systems requires compliance with different international safety standards such as DO-178C. An important consideration of the avionics industry is automated test data generation according to the criteria suggested by safety standards. One of the recommended criteria by DO-178C is the modified condition/decision coverage (MC/DC) criterion. The current model-based test data generation approaches use constraints written in Object Constraint Language (OCL), and apply search techniques to generate test data. These approaches either do not support MC/DC criterion or suffer from performance issues while generating test data for large-scale avionics systems. In this paper, we propose an effective way to automate MC/DC test data generation during model-based testing. We develop a strategy that utilizes case-based reasoning (CBR) and range reduction heuristics designed to solve MC/DC-tailored OCL constraints. We performed an empirical study to compare our proposed strategy for MC/DC test data generation using CBR, range reduction, both CBR and range reduction, with an original search algorithm, and random search. We also empirically compared our strategy with existing constraint-solving approaches. The results show that both CBR and range reduction for MC/DC test data generation outperform the baseline approach. Moreover, the combination of both CBR and range reduction for MC/DC test data generation is an effective approach compared to existing constraint solvers.