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Found 8 papers, 3 papers with code
Can ChatGPT support software verification?
Loop invariant generation is a core task in software verification, and the generation of valid and useful invariants would likely help formal verifiers.
Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes
In contrast, artificial bugs -- produced by mutating existing source code -- can be easily obtained at a sufficient scale and are therefore often preferred in the training of existing approaches.
DeepMutants: Training neural bug detectors with contextual mutations
Learning-based bug detectors promise to find bugs in large code bases by exploiting natural hints such as names of variables and functions or comments.
Modularising Verification Of Durable Opacity
For NOrec, this allows us to re-use an existing opacity proof and complement it with a proof of the durability of accesses to shared state.
Distributed, Parallel, and Cluster Computing
Defining and Verifying Durable Opacity: Correctness for Persistent Software Transactional Memory
In this paper, we transfer the principle of durable concurrent correctness to the area of software transactional memory (STM).
Distributed, Parallel, and Cluster Computing Logic in Computer Science Programming Languages
Testing Monotonicity of Machine Learning Models
This induces an urgent need for quality assurance of ML models with respect to (often domain-dependent) requirements.
Do Android Taint Analysis Tools Keep their Promises?
In yet other cases, the evaluations differ in terms of the data leaks searched for, or lack a ground truth to compare against.
Software Engineering
Predicting Rankings of Software Verification Competitions
Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency.
