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Found 13 papers, 6 papers with code
Predicting Code Coverage without Execution
Using Machine Learning to amortize this expensive process could lower the cost of code coverage by requiring only the source code context, and the task of code coverage prediction can be a novel benchmark for judging the ability of models to understand code.
An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation
This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software.
Exploring and Evaluating Personalized Models for Code Generation
Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code.
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
Ranked #1 on
Cloze Test
on CodeXGLUE - CT-maxmin
GraphCodeBERT: Pre-training Code Representations with Data Flow
Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.
Ranked #1 on
Type prediction
on ManyTypes4TypeScript
Unit Test Case Generation with Transformers and Focal Context
We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3, finding that our approach outperforms GPT-3 and has comparable coverage w. r. t.
Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers
In this paper we present an approach to support developers in writing unit test cases by generating accurate and useful assert statements.
DeepMutation: A Neural Mutation Tool
To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants.
On Learning Meaningful Code Changes via Neural Machine Translation
We show that, when applied in a narrow enough context (i. e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases.
Learning How to Mutate Source Code from Bug-Fixes
Starting from code fixed by developers in the context of a bug-fix, our empirical evaluation showed that our models are able to predict mutants that resemble original fixed bugs in between 9% and 45% of the cases (depending on the model).
Software Engineering
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning.
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities
We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation.
Software Engineering
