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Found 9 papers, 0 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.
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.
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.
AutoTSG: Learning and Synthesis for Incident Troubleshooting
To alleviate these gaps, we investigate the automation of TSGs and propose AutoTSG -- a novel framework for automation of TSGs to executable workflows by combining machine learning and program synthesis.
Landmarks and Regions: A Robust Approach to Data Extraction
We propose a new approach to extracting data items or field values from semi-structured documents.
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.
Information-theoretic User Interaction: Significant Inputs for Program Synthesis
In the context of interactive program synthesis, we use the above result to develop an {\em{active program learner}} that generates the significant inputs to pose as queries to the user in each iteration.
Quantitative Programming by Examples
Programming-by-Example (PBE) systems synthesize an intended program in some (relatively constrained) domain-specific language from a small number of input-output examples provided by the user.
DroidStar: Callback Typestates for Android Classes
We use DroidStar to learn callback typestates for Android classes both for cases where one is already provided by the documentation, and for cases where the documentation is unclear.
