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Found 10 papers, 3 papers with code
Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption.
Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation
We investigate the role of generative AI models in providing human tutor-style programming hints to help students resolve errors in their buggy programs.
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
In our work, we systematically evaluate two models, ChatGPT (based on GPT-3. 5) and GPT-4, and compare their performance with human tutors for a variety of scenarios.
GrACE: Generation using Associated Code Edits
In our experiments with two datasets, the knowledge of prior edits boosts the performance of the LLMs significantly and enables them to generate 29% and 54% more correctly edited code in top-1 suggestions relative to the current state-of-the-art symbolic and neural approaches, respectively.
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming.
Repairing Bugs in Python Assignments Using Large Language Models
We propose to use a large language model trained on code, such as Codex, to build an APR system -- MMAPR -- for introductory Python programming assignments.
Overwatch: Learning Patterns in Code Edit Sequences
Our experiments show that Overwatch has 78% precision and that Overwatch not only completed edits when developers missed the opportunity to use the IDE tool support but also predicted new edits that have no tool support in the IDE.
Synchromesh: Reliable code generation from pre-trained language models
Then, Synchromesh feeds the examples to a pre-trained language model and samples programs using Constrained Semantic Decoding (CSD): a general framework for constraining the output to a set of valid programs in the target language.
Multi-modal Program Inference: a Marriage of Pre-trainedLanguage Models and Component-based Synthesis
Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description.
Learning Syntactic Program Transformations from Examples
In the second domain, we use repetitive edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code.
