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Found 35 papers, 13 papers with code
The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation
Test cases are essential for validating the reliability and quality of software applications.
CodeRepoQA: A Large-scale Benchmark for Software Engineering Question Answering
In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engineering.
Repository-Level Graph Representation Learning for Enhanced Security Patch Detection
To evaluate RepoSPD, we employ two widely-used datasets in security patch detection: SPI-DB and PatchDB.
XRZoo: A Large-Scale and Versatile Dataset of Extended Reality (XR) Applications
The rapid advancement of Extended Reality (XR, encompassing AR, MR, and VR) and spatial computing technologies forms a foundational layer for the emerging Metaverse, enabling innovative applications across healthcare, education, manufacturing, and entertainment.
AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions
Manually writing reproduction scripts is a time-consuming task with high requirements for developers.
Search-Based LLMs for Code Optimization
First, complex optimization methods such as combinatorial ones are hard to be captured by LLMs.
RepoMasterEval: Evaluating Code Completion via Real-World Repositories
Our empirical evaluation on 6 state-of-the-art models shows that test argumentation is critical in improving the accuracy of the benchmark and RepoMasterEval is able to report difference in model performance in real-world scenarios.
XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection
To address this issue, we present \tool, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models.
VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
With recent advancements in Large Multimodal Models (LMMs) across various domains, a novel prompting method called visual referring prompting has emerged, showing significant potential in enhancing human-computer interaction within multimodal systems.
Split and Merge: Aligning Position Biases in LLM-based Evaluators
Specifically, PORTIA splits the answers into multiple segments, aligns similar content across candidate answers, and then merges them back into a single prompt for evaluation by LLMs.
When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection
In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i. e., PositIve and unlabeled Learning mOdel for vulnerability deTection.
LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types
(2) adaptive re-weighting module, which adjusts the learning weights for different types according to the training epochs and numbers of associated samples by a novel training loss.
MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion
On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages.
A Survey on Natural Language Processing for Programming
Natural language processing for programming aims to use NLP techniques to assist programming.
Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis
In this work, we propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more effective cross-domain ABSA.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI).
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.
No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence
Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common.
Generating Tips from Song Reviews: A New Dataset and Framework
However, reviews of music songs are generally long in length and most of them are non-informative for users.
HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems
Due to the complex interactions among multiple options and the high cost of performance measurement under a huge configuration space, it is challenging to study how different configurations influence the system performance.
Source Code Summarization with Structural Relative Position Guided Transformer
We further show that how the proposed SCRIPT captures the structural relative dependencies.
Label-Aware Distribution Calibration for Long-tailed Classification
Real-world data usually present long-tailed distributions.
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification.
Code Structure Guided Transformer for Source Code Summarization
Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance.
Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing
Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22. 3% in terms of F1-score.
CoreGen: Contextualized Code Representation Learning for Commit Message Generation
In this paper, we propose a novel Contextualized code representation learning strategy for commit message Generation (CoreGen).
On the Replicability and Reproducibility of Deep Learning in Software Engineering
Experimental results show the importance of replicability and reproducibility, where the reported performance of a DL model could not be replicated for an unstable optimization process.
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
In this paper, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal) to classify malware with high accuracy and explain the classification result meanwhile.
Automating App Review Response Generation
Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app.
What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society.
CORE: Automating Review Recommendation for Code Changes
The hinge of accurate code review suggestion is to learn good representations for both code changes and reviews.
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models.
Software Engineering
An Online Topic Modeling Framework with Topics Automatically Labeled
In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic.
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations.
Topic Memory Networks for Short Text Classification
Many classification models work poorly on short texts due to data sparsity.
