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Found 22 papers, 10 papers with code
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish
Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks.
Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance
This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming languages.
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning
We introduce a governance framework centered on federated learning (FL), designed to foster the joint development and maintenance of open-source AI code models while safeguarding data privacy and security.
Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More
Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at the input layer of the PLM, without modifying its parameters.
Just-in-Time Security Patch Detection -- LLM At the Rescue for Data Augmentation
To address this pressing issue, we introduce a novel security patch detection system, LLMDA, which capitalizes on Large Language Models (LLMs) and code-text alignment methodologies for patch review, data enhancement, and feature combination.
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
This efficiency, coupled with its state-of-the-art performance, highlights LaFiCMIL's potential as a groundbreaking approach in the field of large file classification.
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
To assess the feasibility of using an LLM as a useful assistant bot for programmers, we must assess its realistic capabilities on unseen problems as well as its capabilities on various tasks.
Letz Translate: Low-Resource Machine Translation for Luxembourgish
We also make use of high-resource languages that are related or share the same linguistic root as the target LRL.
App Review Driven Collaborative Bug Finding
Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews.
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Central to applying ML to software artifacts (like source or executable code) is converting them into forms suitable for learning.
AI-driven Mobile Apps: an Explorative Study
In this paper, we conduct the most extensive empirical study on 56, 682 published AI apps from three perspectives: dataset characteristics, development issues, and user feedback and privacy.
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
To tackle this problem, our intuition is that natural language processing can provide the necessary representations and models for assessing the semantic correlation between a bug (question) and a patch (answer).
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Representation learning of source code is essential for applying machine learning to software engineering tasks.
A two-steps approach to improve the performance of Android malware detectors
For the subset of "difficult" samples, we rely on GUIDED RETRAINING, which leverages the correct predictions and the errors made by the base malware detector to guide the retraining process.
Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We?
Bug reports are common artefacts in software development.
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain.
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0. 557 to 0. 718 and a recall between 0. 562 and 0. 854 in identifying correct patches.
IBIR: Bug Report driven Fault Injection
It remains indeed challenging to inject few but realistic faults that target a particular functionality in a program.
Fault Detection
Program Repair
+2
Software Engineering
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition
In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF).
A First Look at Android Applications in Google Play related to Covid-19
Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world.
Software Engineering Computers and Society
iFixR: Bug Report driven Program Repair
Towards increasing the adoption of patch generation tools by practitioners, we investigate a new repair pipeline, iFixR, driven by bug reports: (1) bug reports are fed to an IR-based fault localizer; (2) patches are generated from fix patterns and validated via regression testing; (3) a prioritized list of generated patches is proposed to developers.
Software Engineering
Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark
Repackaging is a serious threat to the Android ecosystem as it deprives app developers of their benefits, contributes to spreading malware on users' devices, and increases the workload of market maintainers.
Software Engineering
