Search Results for author:
Found 15 papers, 7 papers with code
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.
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.
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.
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.
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.
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
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
Learning the Relation between Code Features and Code Transforms with Structured Prediction
We instantiate our approach in the context of repair transform prediction for Java programs.
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
Watch out for This Commit! A Study of Influential Software Changes
One single code change can significantly influence a wide range of software systems and their users.
Software Engineering
