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.

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

In this study, I empirically and statistically investigate the credibility of common asset pricing 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

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

Sequential testing for efficient and reliable comparison of stochastic algorithms.

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

Project repository for our first study on observing program comprehension with simultaneous fMRI and eye tracking