Suggesting Descriptive Method Names: An ...
Type de document :
Communication dans un congrès avec actes
Titre :
Suggesting Descriptive Method Names: An Exploratory Study of Two Machine Learning Approaches
Auteur(s) :
Zaitsev, Oleksandr [Auteur]
Arolla
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Ducasse, Stephane [Auteur]
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Bergel, Alexandre [Auteur]
Eveillard, Mathieu [Auteur]
Arolla
Arolla
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Ducasse, Stephane [Auteur]
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Bergel, Alexandre [Auteur]
Eveillard, Mathieu [Auteur]
Arolla
Titre de la manifestation scientifique :
QUATIC 2020 - 13th International Conference on the Quality of Information and Communications Technology
Ville :
Faro / Virtual
Pays :
Portugal
Date de début de la manifestation scientifique :
2020-09-08
Mot(s)-clé(s) en anglais :
Software Evolution
Machine Learning
Method Names
Machine Learning
Method Names
Discipline(s) HAL :
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]/Génie logiciel [cs.SE]
Résumé en anglais : [en]
Programming is a form of communication between the person who is writing code and the one reading it. Nevertheless, very often developers neglect readability, and even well-written code becomes less understandable as ...
Lire la suite >Programming is a form of communication between the person who is writing code and the one reading it. Nevertheless, very often developers neglect readability, and even well-written code becomes less understandable as software evolves. Together with the growing complexity of software systems, this creates an increasing need for automated tools for improving the readability of source code. In this work, we focus on method names and study how a descriptive name can be automatically generated from a method's body. We experiment with two approaches from the field of text summarization: One based on TF-IDF and the other on deep recurrent neural network. We collect a dataset of methods from 50 real world projects. We evaluate our approaches by comparing the generated names to the actual ones and report the result using Precision and Recall metrics. For TF-IDF, we get results as good as 28% precision and 45% recall; and for deep neural network, 46% precision and 32% recall.Lire moins >
Lire la suite >Programming is a form of communication between the person who is writing code and the one reading it. Nevertheless, very often developers neglect readability, and even well-written code becomes less understandable as software evolves. Together with the growing complexity of software systems, this creates an increasing need for automated tools for improving the readability of source code. In this work, we focus on method names and study how a descriptive name can be automatically generated from a method's body. We experiment with two approaches from the field of text summarization: One based on TF-IDF and the other on deep recurrent neural network. We collect a dataset of methods from 50 real world projects. We evaluate our approaches by comparing the generated names to the actual ones and report the result using Precision and Recall metrics. For TF-IDF, we get results as good as 28% precision and 45% recall; and for deep neural network, 46% precision and 32% recall.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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