Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.02012 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908515196469248 |
|---|---|
| author | Christensen, Katrine Varshosaz, Mahsa Pardo, Raúl |
| author_facet | Christensen, Katrine Varshosaz, Mahsa Pardo, Raúl |
| contents | Testing probabilistic programs is non-trivial due to their stochastic nature. Given an input, the program may produce different outcomes depending on the underlying stochastic choices in the program. This means testing the expected outcomes of probabilistic programs requires repeated test executions unlike deterministic programs where a single execution may suffice for each test input. This raises the following question: how many times should we run a probabilistic program to effectively test it? This work proposes a novel black-box unit testing method, ProbTest, for testing the outcomes of probabilistic programs. Our method is founded on the theory surrounding a well-known combinatorial problem, the coupon collector's problem. Using this method, developers can write unit tests as usual without extra effort while the number of required test executions is determined automatically with statistical guarantees for the results. We implement ProbTest as a plug-in for PyTest, a well-known unit testing tool for python programs. Using this plug-in, developers can write unit tests similar to any other Python program and the necessary test executions are handled automatically. We evaluate the method on case studies from the Gymnasium reinforcement learning library and a randomized data structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProbTest: Unit Testing for Probabilistic Programs (Extended Version) Christensen, Katrine Varshosaz, Mahsa Pardo, Raúl Software Engineering Testing probabilistic programs is non-trivial due to their stochastic nature. Given an input, the program may produce different outcomes depending on the underlying stochastic choices in the program. This means testing the expected outcomes of probabilistic programs requires repeated test executions unlike deterministic programs where a single execution may suffice for each test input. This raises the following question: how many times should we run a probabilistic program to effectively test it? This work proposes a novel black-box unit testing method, ProbTest, for testing the outcomes of probabilistic programs. Our method is founded on the theory surrounding a well-known combinatorial problem, the coupon collector's problem. Using this method, developers can write unit tests as usual without extra effort while the number of required test executions is determined automatically with statistical guarantees for the results. We implement ProbTest as a plug-in for PyTest, a well-known unit testing tool for python programs. Using this plug-in, developers can write unit tests similar to any other Python program and the necessary test executions are handled automatically. We evaluate the method on case studies from the Gymnasium reinforcement learning library and a randomized data structure. |
| title | ProbTest: Unit Testing for Probabilistic Programs (Extended Version) |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.02012 |