Naplues's Projects
SZZ Algorithm To Detect Fault-Inducing Commits
Contains the code for our ICSE 2020 paper: Big Code != Big Vocabulary: Open-Vocabulary Language Models for Source Code and for its earlier pre-print: Maybe Deep Neural Networks are the Best Choice for Modeling Source Code (https://arxiv.org/abs/1903.05734). This is the first open vocabulary language model for code that uses the byte pair encoding algorithm (BPE) to learn a segmentation of code tokens into subword units.
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)
A website which supports student to find a job.
RAID is a tool pipeline that seamlessly enriches GitHub diff results with refactoring information.
Replicate TSE 2022 paper: How to find actionable static analysis warnings
Accompanying code of "Detecting False Alarms from Automatic Static Analysis Tools: How Far are We?"
The Scott-Knott Effect Size Difference (ESD) test
Feature Selector
Lightweight intraprocedural data and control dependency analysis for Java.
Your library for dynamic language modeling
SpongeBugs: Automatically Generating Fix Suggestions for SonarQube / SpotBugs
The
中文常用停用词表(哈工大停用词表、百度停用词表等)
An implementation of the SZZ algorithm, i.e., an approach to identify bug-introducing commits.
The FPM paper list for the ACM Survey.
This repository proposes the supplementary materials for our paper - Chaff from the Wheat: Characterizing and Determining Valid Bug Reports.
机器学习相关教程
Create a TypeScript Action with tests, linting, workflow, publishing, and versioning
Identifying bugs with prediction models and static analysis