Deep Learning for Time Series Classification

Issue handling for Evidence-based Software Engineering: based on the publicly available data

A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...

Artifact repository for the paper "Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code", In Proceedings of The 46th IEEE/ACM International Conference on Software Engineering (ICSE 2024), Lisbon, Portugal, April 2024

A suite of Julia packages for difference-in-differences

Python framework for automatically executing measurement-based experiments on native and web apps running on Android devices

一个基于中国市场的Fama-French五因子实证研究

Template repository for starting a new empirical paper project implementing good practices for reproducibility using R

Regression-based multi-period difference-in-differences with heterogenous treatment effects

一个基于中国市场的BW投资者情绪指标实证研究

Julia package providing access to the Fama-French data available on the Ken French Data Library

Codes to clean data and construct variables for empirical finance.

The repository contains code and data for the paper https://github.com/sumonbis/ML-Fairness/blob/master/ml-fairness.pdf, to be appeared at ESEC/FSE 2020.

In this study, I empirically and statistically investigate the credibility of common asset # beliefs using data from S&P 500® constituents from January 2010–December 2020.

For our ASE20 paper 🏆 "Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance" (🏆 Distinguished Paper Award!) by Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, and Nachiappan Nagappan

A Sustainable Literature Review for Analyzing the State and Evolution of Empirical Research in Requirements Engineering using KG-EmpiRE.

This is repository contains code for experiment to evaluate catastrophic forgetting in neural networks.

Sequential testing for efficient and reliable comparison of stochastic algorithms.

Testing for Ethereum Smart Contract: An Empirical Study

For our NeurIPS21 paper "Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training" by Shangshu Qian, Hung Viet Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, and Sameena Shah