2020
Roy, Devjeet; Zhang, Ziyi; Ma, Maggie; Arnaoudova, Venera; Panichella, Annibale; Panichella, Sebastiano; Gonzalez, Danielle; Mirakhorli, Mehdi
DeepTC-Enhancer: Improving the Readability of Automatically Generated Tests Inproceedings
In: International Conference on Automated Software Engineering (ASE), pp. 287–298, 2020.
@inproceedings{Devjeet:20:DeepTC-Enhancer,
title = {DeepTC-Enhancer: Improving the Readability of Automatically Generated Tests},
author = {Devjeet Roy and Ziyi Zhang and Maggie Ma and Venera Arnaoudova and Annibale Panichella and Sebastiano Panichella and Danielle Gonzalez and Mehdi Mirakhorli},
url = {http://veneraarnaoudova.com/wp-content/uploads/2020/09/2020-ASE-PREPRINT-DeepTC-Enhancer-Improving-the-Readability-of-Automatically-Generated-Tests.pdf},
year = {2020},
date = {2020-07-30},
booktitle = {International Conference on Automated Software Engineering (ASE)},
pages = {287--298},
keywords = {empirical study, source code readability, source code summarization},
pubstate = {published},
tppubtype = {inproceedings}
}
Roy, Devjeet; Fakhoury, Sarah; Lee, John; Arnaoudova, Venera
A model to detect incremental readability improvements in incremental changes Inproceedings
In: Proceedings of the International Conference on Program Comprehension (ICPC), pp. 25–36, 2020.
@inproceedings{Roy:icpc20:ReadabilityModel,
title = {A model to detect incremental readability improvements in incremental changes},
author = {Devjeet Roy and Sarah Fakhoury and John Lee and Venera Arnaoudova},
url = {http://veneraarnaoudova.com/wp-content/uploads/2020/07/2020-ICPC-PREPRINT-A-Model-to-Detect-Readability-Improvements-in-Incremental-Changes.pdf},
year = {2020},
date = {2020-05-24},
booktitle = {Proceedings of the International Conference on Program Comprehension (ICPC)},
pages = {25--36},
keywords = {developers' perception, empirical study, machine learning, source code readability},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Fakhoury, Sarah; Arnaoudova, Venera; Noiseux, Cedric; Khomh, Foutse; Antoniol, Giuliano
Keep it simple: is deep learning good for linguistic smell detection? Inproceedings
In: Proceedings of the International Conference on Software Analysis, Evolution, and Reengineering (SANER)—REproducibility Studies and NEgative Results (RENE) Track, 2018.
@inproceedings{Fakhoury:saner:CNN,
title = {Keep it simple: is deep learning good for linguistic smell detection?},
author = {Sarah Fakhoury and Venera Arnaoudova and Cedric Noiseux and Foutse Khomh and Giuliano Antoniol},
url = {http://veneraarnaoudova.ca/wp-content/uploads/2018/02/2018-SANER_RENE-preprint-simple-deep-learning.pdf},
year = {2018},
date = {2018-02-22},
booktitle = {Proceedings of the International Conference on Software Analysis, Evolution, and Reengineering (SANER)—REproducibility Studies and NEgative Results (RENE) Track},
keywords = {deep learning, empirical study, linguistic antipatterns, machine learning, source code identifiers, source code readability},
pubstate = {published},
tppubtype = {inproceedings}
}