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Found 10 papers, 5 papers with code
Fast, Fine-Grained Equivalence Checking for Neural Decompilers
Neural decompilers are machine learning models that reconstruct the source code from an executable program.
Are Large Language Models Memorizing Bug Benchmarks?
Large Language Models (LLMs) have become integral to various software engineering tasks, including code generation, bug detection, and repair.
Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
Recent Transformer-based language models have significantly advanced vulnerability detection, surpassing the capabilities of static analysis based deep learning models.
Large Language Models for Test-Free Fault Localization
Specifically, we propose to overcome the left-to-right nature of LLMs by fine-tuning a small set of bidirectional adapter layers on top of the representations learned by LLMs to produce LLMAO, the first language model based fault localization approach that locates buggy lines of code without any test coverage information.
CAT-LM: Training Language Models on Aligned Code And Tests
We also drastically increase the maximum sequence length of inputs to 8, 192 tokens, 4x more than typical code generation models, to ensure that the code context is available to the model when generating test code.
MELT: Mining Effective Lightweight Transformations from Pull Requests
By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in \comby, a language for structural code search and replace.
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection.
Learning to Superoptimize Real-world Programs
We also report that SILO's rate of superoptimization on our test set is over five times that of a standard policy gradient approach and a model pre-trained on compiler optimization demonstration.
A Study on the Challenges of Using Robotics Simulators for Testing
We find that simulation is used by 85% of our participants for testing, and that many participants desire to use simulation as part of their test automation.
Robotics Software Engineering
Effectiveness of Anonymization in Double-Blind Review
Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions.
Digital Libraries General Literature Software Engineering
