Extracting Formal Specifications to Strenghten Type Behaviour Testing
August 17, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dimitri Racordon, Didier Buchs
arXiv ID
1708.05194
Category
cs.PL: Programming Languages
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Testing has become an indispensable activity of software development, yet writing good and relevant tests remains a quite challenging task. One well-known problem is that it often is impossible or unrealistic to test for every outcome, as the input and/or output of a program component can represent incredbly large, unless infinite domains. A common approach to tackle this issue it to only test classes of cases, and to assume that those classes cover all (or at least most) of the cases a component is susceptible to be exposed to. Unfortunately, those kind of assumptions can prove wrong in many situations, causing a yet well-tested program to fail upon a particular input. In this short paper, we propose to leverage formal verification, in particular model checking techniques, as a way to better identify cases for which the aforementioned assumptions do not hold, and ultimately strenghten the confidence one can have in a test suite. The idea is to extract a formal specification of the data types of a program, in the form of a term rewriting system, and to check that specification against a set of properties specified by the programmer. Cases for which tose properties do not hold can then be identified using model checking, and selected as test cases.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
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