Treffer: Testing in the Evolving World of DL Systems: Insights from Python GitHub Projects
Title:
Testing in the Evolving World of DL Systems: Insights from Python GitHub Projects
Authors:
Contributors:
Ali, Q, Riganelli, O, Mariani, L
Publisher Information:
Institute of Electrical and Electronics Engineers Inc.
Publication Year:
2024
Collection:
Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive)
Subject Terms:
Document Type:
Konferenz
conference object
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/isbn/9798350365634; info:eu-repo/semantics/altIdentifier/wos/WOS:001327094200003; ispartofbook:IEEE International Conference on Software Quality, Reliability and Security, QRS; 24th IEEE International Conference on Software Quality, Reliability and Security, QRS 2024 - 1 July 2024 through 5 July 2024; volume:1; firstpage:25; lastpage:35; numberofpages:11; https://hdl.handle.net/10281/522394
DOI:
10.1109/QRS62785.2024.00013
Accession Number:
edsbas.1C3759AE
Database:
BASE
Weitere Informationen
In the ever-evolving field of Deep Learning (DL), ensuring project quality and reliability remains a crucial challenge. This research investigates testing practices within DL projects in GitHub. It quantifies the adoption of testing methodologies, focusing on aspects like test automation, the types of tests (e.g., unit, integration, and system), test suite growth rate, and evolution of testing practices across different project versions. We analyze a subset of 300 carefully selected repositories based on quantitative and qualitative criteria. This study reports insights on the prevalence of testing practices in DL projects within the open-source community.