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Found 12 papers, 3 papers with code
A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection
Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks.
Epicure: Distilling Sequence Model Predictions into Patterns
Most machine learning models predict a probability distribution over concrete outputs and struggle to accurately predict names over high entropy sequence distributions.
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
This approach improves performance in several different settings suggested by prior work, including for two different Large Language Models.
Is Surprisal in Issue Trackers Actionable?
In this study we will propose a new method for unusual event detection in software repositories using surprisal.
Type Inference as Optimization
We are the first to present a detailed algorithm for NTI that is validated with theorems and proofs.
Typilus: Neural Type Hints
The network uses deep similarity learning to learn a TypeSpace -- a continuous relaxation of the discrete space of types -- and how to embed the type properties of a symbol (i. e. identifier) into it.
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
OptTyper combines a continuous interpretation of logical constraints derived by classical static analysis of TypeScript code, with natural constraints obtained from a deep learning model, which learns naming conventions for types from a large codebase.
Model Validation Using Mutated Training Labels: An Exploratory Study
MV mutates training data labels, retrains the model against the mutated data, then uses the metamorphic relation that captures the consequent training performance changes to assess model fit.
SafeStrings: Representing Strings as Structured Data
We introduce SafeStrings to solve this problem and expose latent structure in strings.
Programming Languages
Deep Learning to Detect Redundant Method Comments
To address this problem, we introduce the notion of comment entailment from code, high entailment indicating that a comment's natural language semantics can be inferred directly from the code.
A Survey of Machine Learning for Big Code and Naturalness
We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models.
Tailored Mutants Fit Bugs Better
The results demonstrate that the location selection heuristics produce mutants more closely coupled to real faults for a given budget of mutation operator applications.
Software Engineering
