# Model-based Test Generation for Robotic Software: Automata versus Belief-Desire-Intention Agents

arXiv: https://arxiv.org/abs/1609.08439 · HTML: https://mlsanity.com/paper/1609.08439/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2016.

## Built on by (most cited first, 2 of 2)

- [Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review](https://mlsanity.com/paper/2103.04364/) (2021)
- [Complete Agent-driven Model-based System Testing for Autonomous Systems](https://mlsanity.com/paper/2110.12586/) (2021)

## Abstract (arXiv)

Robotic code needs to be verified to ensure its safety and functional correctness, especially when the robot is interacting with people. Testing real code in simulation is a viable option. However, generating tests that cover rare scenarios, as well as exercising most of the code, is a challenge amplified by the complexity of the interactions between the environment and the software. Model-based test generation methods can automate otherwise manual processes and facilitate reaching rare scenarios during testing. In this paper, we compare using Belief-Desire-Intention (BDI) agents as models for test generation with more conventional automata-based techniques that exploit model checking, in terms of practicality, performance, transferability to different scenarios, and exploration (`coverage'), through two case studies: a cooperative manufacturing task, and a home care scenario. The results highlight the advantages of using BDI agents for test generation. BDI agents naturally emulate the agency present in Human-Robot Interactions (HRIs), and are thus more expressive than automata. The performance of the BDI-based test generation is at least as high, and the achieved coverage is higher or equivalent, compared to test generation based on model checking automata.