This repository contains the data, scripts, and other resources for our paper presented at the 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security (QRS).
If you find this work helpful in your research or projects, please consider citing our paper using the following BibTeX entry:
@INPROCEEDINGS{10684657, author={Ali, Qurban and Riganelli, Oliviero and Mariani, Leonardo}, booktitle={2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS)}, title={Testing in the Evolving World of DL Systems: Insights from Python GitHub Projects}, year={2024}, volume={}, number={}, pages={25-35}, keywords={Deep learning;Software quality;Market research;Software reliability;Maintenance;Security;Standards;Deep learning;Software Testing;Software Quality Assurance;Testing Practice;Open Source;Validation&Verification}, doi={10.1109/QRS62785.2024.00013} }
In this paper, we investigate testing practices in the domain of Deep Learning (DL) systems by analyzing Python-based GitHub projects. Our findings provide valuable insights into the state-of-the-art in DL system testing, identifying gaps and opportunities for improving software quality assurance. We propose a structured framework for analyzing testing approaches in open-source repositories, offering actionable recommendations for developers and researchers.
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This repository is released under the MIT License [https://rem.mit-license.org]. Feel free to use the resources provided here for academic and non-commercial purposes.