5 citations · 10 across the 7 of their papers we have counts for
9 papers
Unsupervised Evaluation of Code LLMs with Round-Trip Correctness
Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin
To evaluate code large language models (LLMs), research has relied on a few small manually curated benchmarks, such as HumanEval and MBPP, which represent a narrow part of the real…
Using Developer Discussions to Guide Fixing Bugs in Software
Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li +1
Automatically fixing software bugs is a challenging task. While recent work showed that natural language context is useful in guiding bug-fixing models, the approach required promp…
CoditT5: Pretraining for Source Code and Natural Language Editing
Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie +2
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 t…
Learning to Describe Solutions for Bug Reports Based on Developer Discussions
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric +1
When a software bug is reported, developers engage in a discussion to collaboratively resolve it. While the solution is likely formulated within the discussion, it is often buried…
Learning to Generate Code Comments from Class Hierarchies
Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie +3
Descriptive code comments are essential for supporting code comprehension and maintenance. We propose the task of automatically generating comments for overriding methods. We formu…
Deep Just-In-Time Inconsistency Detection Between Comments and Source Code
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric +1
Natural language comments convey key aspects of source code such as implementation, usage, and pre- and post-conditions. Failure to update comments accordingly when the correspondi…
Copy that! Editing Sequences by Copying Spans
Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt
Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code. In this paper, we argue t…
Learning to Update Natural Language Comments Based on Code Changes
Sheena Panthaplackel, Pengyu Nie, Milos Gligoric +2
We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns t…
Associating Natural Language Comment and Source Code Entities
Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney +1
Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be usefu…