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Predicting Return Types from Method Names
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10664-014-9350-8openalex-extracted
Linguistic antipatterns: what they are and how developers perceive them
inferredcites →https://doi.org/10.1007/s10664-023-10339-2openalex-extracted
An automated detection of confusing variable pairs with highly similar compound names in Java and Python programs
inferredcites →https://doi.org/10.1016/j.nlp.2023.100013openalex-extracted
A comprehensive review of State-of-The-Art methods for Java code generation from Natural Language Text
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-extracted
An introduction to ROC analysis
inferredcites →https://doi.org/10.1016/s0031-3203(96)00142-2openalex-extracted
The use of the area under the ROC curve in the evaluation of machine learning algorithms
inferredcites →https://doi.org/10.1109/aswec.2018.00026openalex-extracted
Missing Clean Code Acceptance and Support in Practice - An Empirical Study
inferredcites →https://doi.org/10.1109/icse30413.2015openalex-extracted
2015 IEEE/ACM 37th IEEE International Conference on Software Engineering
inferredcites →https://doi.org/10.1109/icse43902.2021.00060openalex-extracted
A Context-Based Automated Approach for Method Name Consistency Checking and Suggestion
inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-extracted
Focal Loss for Dense Object Detection
inferredcites →https://doi.org/10.1109/tse.2022.3178945openalex-extracted
Learning to Predict User-Defined Types
inferredcites →https://doi.org/10.1109/wcre.2009.50openalex-extracted
Relating Identifier Naming Flaws and Code Quality: An Empirical Study
inferredcites →https://doi.org/10.1145/1529282.1529700openalex-extracted
Static type inference for Ruby
inferredcites →https://doi.org/10.1145/2676726.2677009openalex-extracted
Predicting Program Properties from "Big Code"
inferredcites →https://doi.org/10.1145/2786805.2786849openalex-extracted
Suggesting accurate method and class names
inferredcites →https://doi.org/10.1145/2950290.2950343openalex-extracted
Python probabilistic type inference with natural language support
inferredcites →https://doi.org/10.1145/3236024.3236051openalex-extracted
Deep learning type inference
inferredcites →https://doi.org/10.1145/3290353openalex-extracted
code2vec: learning distributed representations of code
inferredcites →https://doi.org/10.1145/332084.332092openalex-extracted
Protecting Java code via code obfuscation
inferredcites →https://doi.org/10.1145/3368089.3409715openalex-extracted
TypeWriter: neural type prediction with search-based validation
inferredcites →https://doi.org/10.1145/3377811.3380926openalex-extracted
Suggesting natural method names to check name consistencies
inferredcites →https://doi.org/10.1145/3416505.3423564openalex-extracted
DeepIaC: deep learning-based linguistic anti-pattern detection in IaC
inferredcites →https://doi.org/10.1145/3468264.3473135openalex-extracted
Learning type annotation: is big data enough?
inferredcites →https://doi.org/10.1145/3510003.3510154openalex-extracted
Learning to recommend method names with global context
inferredcites →https://doi.org/10.1145/3593230openalex-extracted
Evaluating a Large Language Model on Searching for GUI Layouts
inferredcites →https://doi.org/10.18653/v1/p18-1031openalex-extracted
Universal Language Model Fine-tuning for Text Classification
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-extracted
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.4230/oasics.slate.2021.14openalex-extracted
Using Machine Learning for Vulnerability Detection and Classification
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-extracted
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1309.0238openalex-extracted
API design for machine learning software: experiences from the scikit-learn project
inferredcites →https://doi.org/10.48550/arxiv.1609.08144openalex-extracted
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.1909.09436openalex-extracted
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
inferredcites →https://doi.org/10.48550/arxiv.2005.02161openalex-extracted
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2102.12092openalex-extracted
Zero-Shot Text-to-Image Generation
inferredcites →https://doi.org/10.48550/arxiv.2301.03988openalex-extracted
SantaCoder: don't reach for the stars!
inferredcites →https://doi.org/10.48550/arxiv.2308.12950openalex-extracted
Code Llama: Open Foundation Models for Code
inferredcites →https://doi.org/10.52202/068431-0142openalex-extracted
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
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