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Found 18 papers, 13 papers with code
A Transformer-Based Approach for Smart Invocation of Automatic Code Completion
Transformer-based language models are highly effective for code completion, with much research dedicated to enhancing the content of these completions.
Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study
However, research on code completion models typically focuses on imperative languages such as Python and JavaScript, which results in a lack of representation for functional programming languages.
An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets
Based on the findings of our study, which highlights the pervasive issue of license inconsistencies in large language models trained on code, our recommendation for both researchers and the community is to prioritize the development and adoption of best practices for dataset creation and management.
Language Models for Code Completion: A Practical Evaluation
InCoder outperformed the other models across all programming languages, highlighting the significance of training data and objectives.
Traces of Memorisation in Large Language Models for Code
We find that large language models for code are vulnerable to data extraction attacks, like their natural language counterparts.
On the Impact of Language Selection for Training and Evaluating Programming Language Models
The recent advancements in Transformer-based Language Models have demonstrated significant potential in enhancing the multilingual capabilities of these models.
Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study
For comments, we find that the models perform better in the presence of multi-line comments (again with small effect sizes).
The (ab)use of Open Source Code to Train Large Language Models
In recent years, Large Language Models (LLMs) have gained significant popularity due to their ability to generate human-like text and their potential applications in various fields, such as Software Engineering.
STACC: Code Comment Classification using SentenceTransformers
Code comments are a key resource for information about software artefacts.
Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge
In this work, we apply a targeted data extraction attack to the SATML2023 Language Model Training Data Extraction Challenge.
Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries
While the automated summarisation of decompiled code can help Reverse Engineers understand and analyse binaries, current work mainly focuses on summarising source code, and no suitable dataset exists for this task.
An Empirical Study on Data Leakage and Generalizability of Link Prediction Models for Issues and Commits
However, these approaches primarily focused on improving prediction accuracy on randomly-split datasets, with limited attention given to the impact of data leakage and the generalizability of the predictive models.
Semantically-enhanced Topic Recommendation System for Software Projects
Then, (2) we build two recommender systems; The first one operates only based on the list of original topics assigned to a repository and the relationships specified in our knowledge graph.
On the Evaluation of NLP-based Models for Software Engineering
NLP-based models have been increasingly incorporated to address SE problems.
CatIss: An Intelligent Tool for Categorizing Issues Reports using Transformers
Then, the pre-trained RoBERTa model is fine-tuned on the preprocessed dataset.
CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences
Both approaches have significant drawbacks: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand the semantics of the programming language and the developer's code context.
Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data
The low performance gets even more severe when there is a lack of textual information in either commits or issues.
Improving Quality of a Post's Set of Answers in Stack Overflow
Then, we developed an Eclipse plugin named SOPI and integrated the prediction model in the plugin to link these deficient posts to related developers and help them improve the answer set.
