PROZE: Generating Parameterized Unit Tests Informed by Runtime Data
June 30, 2024 Β· Declared Dead Β· π IEEE Working Conference on Source Code Analysis and Manipulation
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Deepika Tiwari, Yogya Gamage, Martin Monperrus, Benoit Baudry
arXiv ID
2407.00768
Category
cs.SE: Software Engineering
Citations
1
Venue
IEEE Working Conference on Source Code Analysis and Manipulation
Last Checked
5 months ago
Abstract
Typically, a conventional unit test (CUT) verifies the expected behavior of the unit under test through one specific input / output pair. In contrast, a parameterized unit test (PUT) receives a set of inputs as arguments, and contains assertions that are expected to hold true for all these inputs. PUTs increase test quality, as they assess correctness on a broad scope of inputs and behaviors. However, defining assertions over a set of inputs is a hard task for developers, which limits the adoption of PUTs in practice. In this paper, we address the problem of finding oracles for PUTs that hold over multiple inputs. We design a system called PROZE, that generates PUTs by identifying developer-written assertions that are valid for more than one test input. We implement our approach as a two-step methodology: first, at runtime, we collect inputs for a target method that is invoked within a CUT; next, we isolate the valid assertions of the CUT to be used within a PUT. We evaluate our approach against 5 real-world Java modules, and collect valid inputs for 128 target methods from test and field executions. We generate 2,287 PUTs, which invoke the target methods with a significantly larger number of test inputs than the original CUTs. We execute the PUTs and find 217 that provably demonstrate that their oracles hold for a larger range of inputs than envisioned by the developers. From a testing theory perspective, our results show that developers express assertions within CUTs that are general enough to hold beyond one particular input.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted