machine learning

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.
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license
machine learning
empirical studies
classifier

Do Developers Discuss Design?

Submitted by msquire on Fri, 2014-08-01 19:12
J. Brunet, Murphy, G. C., Terra, R., Figueiredo, J., and Serey, D., “Do Developers Discuss Design?”, in Proceedings of the 11th Working Conference on Mining Software Repositories, New York, NY, USA, 2014, pp. 340–343.
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msr challenge
mining challenge
machine learning
empirical study
Design Discussions

Who should fix this bug?

Submitted by msquire on Wed, 2012-08-01 11:27
J. Anvik, Hiew, L., and Murphy, G. C., “Who should fix this bug?”, in Proceedings of the 28th international conference on Software engineering, New York, NY, USA, 2006, pp. 361–370.
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bug fixing
eclipse
Firefox
bug report
gcc
machine learning
bug report assignment
bug triage
issue tracking
problem tracking

Summarizing software artifacts: a case study of bug reports

Submitted by msquire on Wed, 2012-08-01 10:58
S. Rastkar, Murphy, G. C., and Murray, G., “Summarizing software artifacts: a case study of bug reports”, in Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1, New York, NY, USA, 2010, pp. 505–514.
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Mozilla
eclipse
gnome
bug reports
kde
machine learning
human-centric software engineering

A Preliminary Analysis of Publicly Available FLOSS Measurements: Towards Discovering Maintainability Trends

Submitted by msquire on Sat, 2011-04-30 16:50
I. Samoladas, Bibi, S., Stamelos, I., Sowe, S. K., and Deligiannis, I., “A Preliminary Analysis of Publicly Available FLOSS Measurements: Towards Discovering Maintainability Trends”, in 2nd Workshop on Public Data about Software Development (WoPDaSD 2007), 2007.
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java
flossmole
metrics
sourcekibitzer
machine learning
decision tree