Tesis y Tesistas 2020 - Postgrado - Fac. de Informática - UNLP

MAESTRÍA
INGENIERÍA DE SOFTWARE
Mg. Mauro Gullino
e-mail
maurogullino@gmail.com
english
Advisor
Dr. Gabriela Robiolo
Codirector
Dr. Gustavo Héctor Rossi
Thesis defense date
March 26, 2020
Predicting faults in a dynamic
typed language using static
and dynamic metrics
SEDICI
http://sedici.unlp.edu.ar/handle/10915/93237
Keywords: Software Metrics; Code Metrics; Change Metrics; Defects Prediction; Logistic regression
Motivation
The purpose of this investigation is to study the
feasibility of applying classical static metrics and (more
current) change metrics in a product developed with a
dynamically typed language, in contrast to static typed
languages, which are the ones that have mainly been
studied in the literature. Little research is verified on
those types of languages which, however, are of great
interest to the industry.
Future Research Lines
It is of interest to increase the amount of historical
data used within the prediction, in order to detect how
the evolution in the language typing system affects
the results. On the other hand, the same study can be
replicated in other dynamically typed languages widely
used in the industry.
Thesis contributions
In the present work, an existing static metrics framework
was analyzed and adapted, along with other classic static
metrics and more modern change metrics, for application
in a dynamically typed language. Subsequently,
computer tools were developed to obtain the metrics of
the MediaWiki project. Finally, logistic regression models
were constructed to find the set of metrics that works as
the best defects predictor in a class. It is demonstrated
that the metrics of quantity and size of the changes
introduced in a class (change metrics) constitute the
best predictor, which conforms with previous works in the
field. The case study provides evidence that the analyzed
metrics are applicable to dynamically typed languages in
order to predict defects.
79 TESIS Y TESISTAS 2020