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Found 11 papers, 7 papers with code
Learning to Update Natural Language Comments Based on Code Changes
We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies.
CoditT5: Pretraining for Source Code and Natural Language Editing
Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits.
Learning Deep Semantics for Test Completion
We formalize the novel task of test completion to automatically complete the next statement in a test method based on the context of prior statements and the code under test.
Deep Generation of Coq Lemma Names Using Elaborated Terms
Our results show that Roosterize substantially outperforms baselines for suggesting lemma names, highlighting the importance of using multi-input models and elaborated terms.
Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning
Naming conventions are an important concern in large verification projects using proof assistants, such as Coq.
Multilingual Code Co-Evolution Using Large Language Models
In this paper, we target a novel task: translating code changes from one programming language to another using large language models (LLMs).
Impact of Evaluation Methodologies on Code Summarization
This may lead to evaluations that are inconsistent with the intended use cases.
Learning to Format Coq Code Using Language Models
Should arguments to the rewrite tactic be separated by a single space?
Learning to Generate Code Comments from Class Hierarchies
Descriptive code comments are essential for supporting code comprehension and maintenance.
Leveraging Class Hierarchy for Code Comprehension
Object-oriented programming languages enable a hierarchical class structure, which provides rich contextual information to guide code comprehension and synthesis.
A Framework for Multi-stage Bonus Allocation in meal delivery Platform
The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to order and its acceptance probability, the Lagrangian dual-based dynamic programming algorithm aims to calculate the empirical Lagrangian multiplier for each allocation stage offline based on the historical data set, and the online allocation algorithm uses the results attained in the offline part to calculate a proper delivery bonus for each order.
