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TypeWriter: Neural Type Prediction with Search-based Validation
[article]
2020
arXiv
pre-print
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TypeWriter's predictor learns to infer the return and argument types for functions from partially annotated code bases by combining the natural language properties of code with programming language-level ...
information. ...
The model hence learns to predict "unknown" whenever none of the types in the vocabulary fit the given context information. ...
arXiv:1912.03768v2
fatcat:uiy6mvwfuvfvni5w23fjwy6jcq
Repository-Level Prompt Generation for Large Language Models of Code
[article]
2022
arXiv
pre-print
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In this work, we propose a framework called Repo-Level Prompt Generator that learns to generate example-specific prompts using a set of rules. ...
These rules take context from the entire repository, thereby incorporating both the structure of the repository and the context from other relevant files (e.g. imports, parent class files). ...
We would also like to extend our thanks to Breandan Considine for help in crawling the Google Code data archives; Justine Gehring, Avinash Bhat and Breandan Considine for helping with resources for running ...
arXiv:2206.12839v1
fatcat:3j5zjqh3vvhrzisd6dxrbddkhm
Review4Repair: Code Review Aided Automatic Program Repairing
[article]
2020
arXiv
pre-print
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Context: Learning-based automatic program repair techniques are showing promise to provide quality fix suggestions for detected bugs in the source code of the software. ...
However, none of the learning-based tools has utilized the review comments to fix programming bugs to the best of our knowledge. ...
Model learns to predict code change ( f ), from code before change (C d ), defect location, and code review comment (R). ...
arXiv:2010.01544v2
fatcat:47yu7qdlebgavfo64r5dtbidqu
Towards Learning Generalizable Code Embeddings using Task-agnostic Graph Convolutional Networks
2022
ACM Transactions on Software Engineering and Methodology
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Our findings suggest that future research and practice may consider using graph-based deep learning methods to capture the structural information of the source code for SE tasks. ...
To evaluate the effectiveness of GraphCodeVec , we consider three downstream benchmark tasks (i.e., code comment generation, code authorship identification, and code clones detection) that are used in ...
Diferent from CBOW which utilizes the context words to predict the target one, Skip-gram model tries to predict the surrounding context words given the target word. ...
doi:10.1145/3542944
fatcat:lthri6wilzgzdplgvaqgszl4au
Learning to Update Natural Language Comments Based on Code Changes
[article]
2020
arXiv
pre-print
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We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code ...
We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. ...
Namely, our model is trained to generate a sequence of edit actions, which are to be applied to the existing comment, by conditioning on learned representations of the code edits and existing comment. ...
arXiv:2004.12169v2
fatcat:bn2zlb62njhf3ap7dp2himebyy
A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques
2020
Security and Communication Networks
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At the same time, we point out some problems of these research methods, give corresponding solutions, and finally look forward to the application prospect of deep learning technology in automated software ...
vulnerability detection, automated program repair, and automated defect prediction. ...
In the previous forecasting model, only the program code data was focused on, and the program code comment information was rarely paid attention to. ...
doi:10.1155/2020/8858010
fatcat:obeiw4p7afan5m24ydmdkmyhbm
Guest Editorial: Knowledge Discovery for Software Development (KDSD)
2020
IET Software
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Acknowledgments We are grateful to The Journal of IET Software Editor-in-Chief and the Editorial Office for their support throughout the editorial process. ...
We would like to oblige all participants, Committee members, and the reviewers for this Special Issue of IET Software, for their dedication and hard work. ...
to integrate domain of Machine Learning (ML), Statistical learning techniques, and Information Retrieval techniques. ...
doi:10.1049/iet-sen.2020.0166
fatcat:ixaksxisz5g55ml2arl2uvgzu4
A Survey of Machine Learning for Big Code and Naturalness
2018
ACM Computing Surveys
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Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit ...
We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. ...
to model context information. ...
doi:10.1145/3212695
fatcat:iuuocyctg5adjmobhc2zw23rfu
InCoder: A Generative Model for Code Infilling and Synthesis
[article]
2022
arXiv
pre-print
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Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming ...
bidirectional context. ...
Acknowledgments We thank the Code Clippy team for open sourcing their data acquisition and deduplication code, 19 which we used as a starting point for our corpus collection. ...
arXiv:2204.05999v2
fatcat:tfefjg5ws5g3vm2iuc625kk2ey
Hippocampal Representational Organization and Spatial Context
1999
Hippocampus
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The specific contribution of the hilar/CA3 region is suggested to be to compare the expected spatial context with that currently being experienced, then relay discrepancies to CA1. ...
In this way, hippocampus helps to distinguish temporally one spatial context from another, thereby contributing to episodic memories. Hippocampus 1999;9:444-451. ...
Acknowledgments We thank James Canfield and Wayne Pratt for comments regarding this manuscript. ...
doi:10.1002/(sici)1098-1063(1999)9:4<444::aid-hipo10>3.0.co;2-z
pmid:10495025
fatcat:ug7lvg5zwbdttdvovtabufu5jm
Hippocampal Representational Organization and Spatial Context
1999
Hippocampus
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The specific contribution of the hilar/CA3 region is suggested to be to compare the expected spatial context with that currently being experienced, then relay discrepancies to CA1. ...
In this way, hippocampus helps to distinguish temporally one spatial context from another, thereby contributing to episodic memories. Hippocampus 1999;9:444-451. ...
Acknowledgments We thank James Canfield and Wayne Pratt for comments regarding this manuscript. ...
doi:10.1002/(sici)1098-1063(1999)9:4<444::aid-hipo10>3.3.co;2-q
pmid:10495025
fatcat:xkjjsl3irfg3nbru3hcnlleymu
A Survey of Machine Learning for Big Code and Naturalness
[article]
2018
arXiv
pre-print
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2017 pre-print arXiv:1709.06182v1 fatcat:3pxzp44os5gmbodct2ft443gby
Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit ...
We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. ...
to model context information. ...
arXiv:1709.06182v2
fatcat:hbvgyonqsjgq3nqwji6jf3aybe
Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning
[article]
2022
arXiv
pre-print
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Our main insight is instead of modeling what a repair edit should look like, we can directly predict what the correct code is based on the context information. ...
Therefore, in this paper, we aim to revisit the learning-based APR problem, and propose AlphaRepair, to leverage zero-shot learning directly using large pre-trained code models for APR. ...
We build AlphaRepair using Code-BERT and design inputs to make use of the pre-training objective of CodeBERT to directly generate fix lines from the surrounding context. ...
arXiv:2207.08281v2
fatcat:5eucjvil3vdrhollpdoa6l7hzm
Improving automatic source code summarization via deep reinforcement learning
2018
Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering - ASE 2018
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The actor network provides the confidence of predicting the next word according to current state. ...
trained to predict the next word by maximizing the likelihood of next groundtruth word with previous ground-truth word given. ...
LSTM model [18] to represent the structure of code. We also use another LSTM model [42] to represent the sequential information of code. ...
doi:10.1145/3238147.3238206
dblp:conf/kbse/WanZYXY0Y18
fatcat:eknsug7narft5dre5ncenn2nvm
Dipole
2017
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17
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2017 pre-print arXiv:1706.05764v1 fatcat:sjvjyxkp7jctfesrg7nvqjbehy
Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. ...
To address these issues, we propose Dipole, an end-to-end, simple and robust model for predicting patients' future health information. ...
ACKNOWLEDGMENTS e authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. is work is supported in part by the US National Science Foundation under grants ...
doi:10.1145/3097983.3098088
dblp:conf/kdd/MaCZYSG17
fatcat:6sth7fjv7bcwlhsfzl4zu6oz6u
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