You are here

empirical studies

Machine Learning-Based Detection of Open Source License Exceptions

Submitted by msquire on Wed, 2017-05-24 20:54

C. Vendome, Linares-Vasquez, M., Bavota, G., Di Penta, M., German, D. M., and Poshyvanyk, D., “Machine Learning-Based Detection of Open Source License Exceptions”, 2017 IEEE/ACM 39th International Conference on Software Engineering. pp. 118-129, 2017.

Taxonomy upgrade extras:

machine learning

empirical studies

Read more about Machine Learning-Based Detection of Open Source License Exceptions
Log in or register to post comments
Google Scholar
DOI
BibTeX
Tagged
EndNote XML

Comparison Between SLOCs and Number of Files As Size Metrics for Software Evolution Analysis

Submitted by msquire on Mon, 2014-05-19 13:07

I. Herraiz, Robles, G., and us Gonzalez-Barahon, J. M., “Comparison Between SLOCs and Number of Files As Size Metrics for Software Evolution Analysis”, in Proceedings of the Conference on Software Maintenance and Reengineering, Washington, DC, USA, 2006, pp. 206–213.

Taxonomy upgrade extras:

software evolution

empirical studies

Read more about Comparison Between SLOCs and Number of Files As Size Metrics for Software Evolution Analysis
Log in or register to post comments
Google Scholar
BibTeX
Tagged
EndNote XML