Banca de DEFESA: RAMON ARAÚJO GOMES

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : RAMON ARAÚJO GOMES
DATE: 18/08/2021
TIME: 14:00
LOCAL: On Line - Sala do PGcomp
TITLE:

Antecipanting Technical Debt Items in Model Driven Development Projects

KEY WORDS:

Model Driven Development, Technical Debt, Bad Smell

PAGES: 117
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Engenharia de Software
SUMMARY:

ABSTRACT

Model Driven Development (MDD) and the Technical Debt (TD) metaphor are software

engineering approaches that look for promoting quality of systems under development.

While MDD does it through the use of models as primary artifacts in the software development

process, TD management does it by correctly dealing with problems that, out

of control, can impair system maintenance and evolution. Most research on TD focuses

on application code as primary TD sources. In an MDD project, however, dealing with

technical debt only on the source code may not be an adequate strategy because code

generation is often done at a later stage than creating models, and then dealing with TD

only in source code can lead to unnecessary interest payments due to unmanaged debts.

Recent works concluded that MDD project codes are not technical debt free, making it

necessary to investigate the possibility and bene ts of applying TD identi cation techniques

in earlier stages of the development process, such as in modeling phases. This

work intends to analyze whether it is possible to use source code technical debt detection

strategies to identify TD on code-generating models in the context of model-driven development

projects. For this, we investigated source code TD detection techniques were

evaluated with the purpose of adapting them to be used in the model abstraction level.

A catalog of nine di erent model technical debt items for platform-independent codegenerating

models was speci ed, with detection strategies that can automate the TD

identi cation process in the models. Then, the proposed detection strategies were implemented

in a tool that allows both speci cation and automatic identi cation of so-called

model smells in EMF models. A preliminary study was performed to evaluate and re ne

the proposed detection strategies by using 3 opensource projects versioned on Github.

Then, a deeper experimental study was performed in order to evaluate the detection strategies

real e ectiveness in anticipating the technical debt identi cation, detecting them

in the code-generating models. A total of 9 di erent projects, with 36 EMF models and

more than 400 thousand lines of code were used in this evaluation, all opensource, git

versioned and developed with EMF technology. Results showed that the evaluated detection

strategies are able to anticipate a great amount of source code technical debt in the

code-generating models. However, di erent catalog items presented di erent anticipation

e ectiveness levels and various levels of occurrences in the studied projects. A discussion

was performed for each evaluated item in order to explain the obtained results. Finally,

aspects were obtained that may guide future works in improvements and also extension

of the catalog.

BANKING MEMBERS:
Presidente - 1708274 - RITA SUZANA PITANGUEIRA MACIEL
Interno - 1710389 - CLAUDIO NOGUEIRA SANT ANNA
Externo à Instituição - UIRÁ KULESZA - UFRN