Papers Variable misuse
“Variable misuse” 태그가 달린 논문 15편 · 필터 해제
The Power of Types: Exploring the Impact of Type Checking on Neural Bug Detection in Dynamically Typed Languages
Motivation: Automated bug detection in dynamically typed languages such as Python is essential for maintaining code quality. The lack of mandatory type annotations in such languages can lead to errors that are challengin…
Variable misuseA Critical Study of What Code-LLMs (Do Not) Learn
Large Language Models trained on code corpora (code-LLMs) have demonstrated impressive performance in various coding assistance tasks. However, despite their increased size and training dataset, code-LLMs still have limi…
Variable misuseNeuro-Symbolic Execution of Generic Source Code
Can a Python program be executed statement-by-statement by neural networks composed according to the source code? We formulate the Neuro-Symbolic Execution Problem and introduce Neural Interpretation (NI), the first neur…
Variable misuseWhat does Transformer learn about source code?
In the field of source code processing, the transformer-based representation models have shown great powerfulness and have achieved state-of-the-art (SOTA) performance in many tasks. Although the transformer models proce…
Variable misuseProbing Pretrained Models of Source Code
Deep learning models are widely used for solving challenging code processing tasks, such as code generation or code summarization. Traditionally, a specific model architecture was carefully built to solve a particular co…
ClassificationregressionVariable misuseGraph Conditioned Sparse-Attention for Improved Source Code Understanding
Transformer architectures have been successfully used in learning source code representations. The fusion between a graph representation like Abstract Syntax Tree (AST) and a source code sequence makes the use of current…
Code SummarizationVariable misuseCodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
Designing a suitable representation for code-reasoning tasks is challenging in aspects such as the kinds of program information to model, how to combine them, and how much context to consider. We propose CodeTrek, a deep…
Exception typeVariable misuseMemorization and Generalization in Neural Code Intelligence Models
Deep Neural Networks (DNNs) are increasingly being used in software engineering and code intelligence tasks. These are powerful tools that are capable of learning highly generalizable patterns from large datasets through…
Code Documentation GenerationCode SearchMemorizationMethod name prediction+1Understanding Neural Code Intelligence Through Program Simplification
A wide range of code intelligence (CI) tools, powered by deep neural networks, have been developed recently to improve programming productivity and perform program analysis. To reliably use such tools, developers often n…
Method name predictionVariable misuseLearning to Extend Program Graphs to Work-in-Progress Code
Source code spends most of its time in a broken or incomplete state during software development. This presents a challenge to machine learning for code, since high-performing models typically rely on graph structured rep…
Code CompletionVariable misuseLearning Graph Structure With A Finite-State Automaton Layer
Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relational structure (edges) between nodes in th…
Variable misuseLearning Semantic Program Embeddings with Graph Interval Neural Network
Learning distributed representations of source code has been a challenging task for machine learning models. Earlier works treated programs as text so that natural language methods can be readily applied. Unfortunately, …
Graph Neural NetworkMethod name predictionVariable misuseGlobal Relational Models of Source Code
Models of code can learn distributed representations of a program's syntax and semantics to predict many non-trivial properties of a program. Recent state-of-the-art models leverage highly structured representations of p…
Inductive BiasProgram RepairVariable misuseLearning and Evaluating Contextual Embedding of Source Code
Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A significant advancement in natural-language unde…
Contextual Embedding for Source CodeException typeFunction-docstring mismatchNatural Language Understanding+4Neural Program Repair by Jointly Learning to Localize and Repair
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of bug…
Program RepairVariable misuse