17 research outputs found
Trust in MDE Components: the DOMINO Experiment
Get PDFInternational audienceA large number of modeling activities can be automatic or computer assisted. This automation ensures a more rapid and robust software development. However, engineers must ensure that the models have the properties required for the application. In order to tend towards this requirement, the DOMINO project (DOMaINs and methodological prOcess) proposes to use the socalled trustworthy Model-Driven Engineering (MDE) components and aims to provide a methodology for the validation and qualification of such components
Static Analysis of Model Transformations for Effective Test Generation
Get PDFInternational audienceModel transformations are an integral part of several computing systems that manipulate interconnected graphs of objects called models in an input domain specified by a metamodel and a set of invariants. Test models are used to look for faults in a transformation. A test model contains a specific set of objects, their interconnections and values for their attributes. Can we automatically generate an effective set of test models using knowledge from the transformation? We present a white-box testing approach that uses static analysis to guide the automatic generation of test inputs for transformations. Our static analysis uncovers knowledge about how the input model elements are accessed by transformation operations. This information is called the input metamodel footprint due to the transformation. We transform footprint, input metamodel, its invariants, and transformation pre-conditions to a constraint satisfaction problem in Alloy. We solve the problem to generate sets of test models containing traces of the footprint. Are these test models effective? With the help of a case study transformation we evaluate the effectiveness of these test inputs. We use mutation analysis to show that the test models generated from footprints are more effective (97.62% avg. mutation score) in detecting faults than previously developed approaches based on input domain coverage criteria (89.9% avg.) and unguided generation (70.1% avg.)
Model Transformation Testing and Debugging: A Survey
Get PDFModel transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state of the art.
This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This paper presents a survey on testing and debugging model transformations based on the analysis of \nPapers~papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorise the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.This work is partially supported by the European Commission (FEDER) and Junta de Andalucia under projects APOLO (US-1264651) and
EKIPMENT-PLUS (P18-FR-2895), by the Spanish Government (FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación) under projects HORATIO (RTI2018-101204-B-C21), COSCA (PGC2018-094905-B-I00) and LOCOSS (PID2020-114615RB-I00), by the Austrian Science Fund (P 28519-N31, P 30525-N31), and by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development (CDG
Featured Model-based Mutation Analysis
Get PDFInternational audienceModel-based mutation analysis is a powerful but expensive testing technique. We tackle its high computation cost by proposing an optimization technique that drastically speeds up the mutant execution process. Central to this approach is the Featured Mutant Model, a modelling framework for mutation analysis inspired by the software product line paradigm. It uses behavioural variability models, viz., Featured Transition Systems, which enable the optimized generation, configuration and execution of mutants. We provide results, based on models with thousands of transitions, suggesting that our technique is fast and scalable. We found that it outperforms previous approaches by several orders of magnitude and that it makes higher-order mutation practically applicable
Un entorno de pruebas de mutación en Eclipse
Full text linkUna
técnica
para
medir
la
efectividad
de
un
conjunto
de
casos
de
prueba
y
ayudar
a
mejorarlo
son
las
pruebas
de
mutación.
En
las
pruebas
de
este
tipo,
se
insertan
fallos
en
un
programa
para
obtener
distintas
versiones
erróneas
del
mismo.
Al
error
introducido
en
el
programa
se
le
denomina
mutación
y
a
las
nuevas
versiones
del
programa
inicial
se
les
llama
mutantes,
y
se
utilizan
para
comprobar
si,
dado
un
conjunto
de
casos
de
prueba,
éste
es
capaz
de
detectar
los
fallos
introducidos
en
cada
mutante.
En
caso
de
no
detectarlos,
el
desarrollador
debe
proporcionar
nuevos
casos
de
prueba
que
detecten
los
mutantes
creados,
mejorando
de
este
modo
la
calidad
del
conjunto
de
casos
de
prueba
inicial.
El
objetivo
de
este
Trabajo
de
Fin
de
Grado
es
construir
una
herramienta
de
mutación
que
genere
de
forma
automática
mutantes
de
un
programa
escrito
en
el
lenguaje
ATL
(Atlas
Transformation
Language),
para
posteriormente
poder
medir
la
eficacia
del
conjunto
de
casos
de
prueba
diseñado
para
probar
el
programa.
ATL
es
un
lenguaje
de
programación
para
definir
transformaciones
de
modelos,
que
son
un
tipo
de
programa
software
cuyos
argumentos
de
entrada
y
salida
son
modelos.
Se
utilizan
dentro
del
paradigma
de
Desarrollo
Dirigido
por
Modelos,
que
es
un
método
de
desarrollo
de
software
en
el
que
los
datos
que
se
manejan
son
modelos.
Las
mutaciones
que
se
generan
y
describen
en
el
presente
trabajo
están
orientadas
a
este
tipo
de
software.Mutation
testing
is
a
technique
to
measure
the
efficacy
of
a
set
of
test
cases
and
help
to
improve
it.
On
this
kind
of
testing,
faults
are
injected
into
a
program
to
get
faulty
versions
of
it.
Errors
introduced
in
the
program
are
called
mutations
and
the
new
versions
of
the
original
program
are
called
mutants,
and
they
are
used
to
check
whether,
given
a
set
of
test
cases,
this
is
able
to
detect
the
faults
introduced
in
each
mutant.
If
they
are
not
detected,
the
developer
must
provide
new
test
cases
that
detect
the
created
mutants,
thus
improving
the
overall
quality
of
the
initial
test
set.
The
objective
of
this
Bachelor’s
Project
is
to
build
a
mutation
tool
to
generate
automatically
mutants
of
a
program
written
with
the
ATL
language
(Atlas
Transformation
Language),
and
measure
the
efficacy
of
a
set
of
test
cases
designed
for
testing
the
program.
ATL
is
a
programming
language
to
implement
model
transformations,
which
are
programs
whose
input
and
output
arguments
are
models.
They
are
used
in
the
context
of
Model
Driven
Development,
which
is
a
software
development
method
where
the
manipulated
data
are
models.
The
mutations
generated
and
described
on
this
work
are
oriented
to
this
kind
of
software
A Multi-Level Framework for the Detection, Prioritization and Testing of Software Design Defects
Full text linkLarge-scale software systems exhibit high complexity and become difficult to maintain. In fact, it has been reported that software cost dedicated to maintenance and evolution activities is more
than 80% of the total software costs. In particular, object-oriented software systems need to
follow some traditional design principles such as data abstraction, encapsulation, and modularity.
However, some of these non-functional requirements can be violated by developers for many
reasons such as inexperience with object-oriented design principles, deadline stress. This high
cost of maintenance activities could potentially be greatly reduced by providing automatic or
semi-automatic solutions to increase system‟s comprehensibility, adaptability and extensibility to
avoid bad-practices.
The detection of refactoring opportunities focuses on the detection of bad smells, also called
antipatterns, which have been recognized as the design situations that may cause software
failures indirectly. The correction of one bad smell may influence other bad smells. Thus, the
order of fixing bad smells is important to reduce the effort and maximize the refactoring benefits.
However, very few studies addressed the problem of finding the optimal sequence in which the
refactoring opportunities, such as bad smells, should be ordered. Few other studies tried to
prioritize refactoring opportunities based on the types of bad smells to determine their severity.
However, the correction of severe bad smells may require a high effort which should be
optimized and the relationships between the different bad smells are not considered during the
prioritization process.
The main goal of this research is to help software engineers to refactor large-scale systems with a
minimum effort and few interactions including the detection, management and testing of
refactoring opportunities. We report the results of an empirical study with an implementation of
our bi-level approach. The obtained results provide evidence to support the claim that our
proposal is more efficient, on average, than existing techniques based on a benchmark of 9 open
source systems and 1 industrial project. We have also evaluated the relevance and usefulness of
the proposed bi-level framework for software engineers to improve the quality of their systems
and support the detection of transformation errors by generating efficient test cases.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136075/1/Dilan_Sahin_Final Dissertation.pdfDescription of Dilan_Sahin_Final Dissertation.pdf : Dissertatio
Towards an Automation of the Mutation Analysis Dedicated to Model Transformation
Get PDFInternational audienceA major benefit of Model Driven Engineering (MDE) relies on the automatic generation of artefacts from high-level models through intermediary levels using model transformations. In such a process, the input must be well-designed and the model transformations should be trustworthy. Due to the specificities of models and transformations, classical software test techniques have to be adapted. Among these techniques, mutation analysis has been ported and a set of mutation operators has been defined. However, mutation analysis currently requires a considerable manual work and suffers from the test data set improvement activity. This activity is seen by testers as a difficult and time-consuming job, and reduces the benefits of the mutation analysis. This paper addresses the test data set improvement activity. Model transformation traceability in conjunction with a model of mutation operators, and a dedicated algorithm allow to automatically or semi-automatically produce test models that detect new faults. The proposed approach is validated and illustrated in a case study written in Kermeta
Estado del arte de verificación de transformación de modelos
Get PDFEl Desarrollo de Software Guiado por Modelos (Model-Driven Development, MDD) es un enfoque de ingenierÃa de software basado en el modelado de un sistema como la principal actividad del desarrollo y la construcción del mismo guiada por transformaciones de dichos modelos. Su éxito depende fuertemente de la disponibilidad de lenguajes y herramientas apropiados para realizar las transformaciones y validar su corrección. En relación a este último punto, este documento presenta un relevamiento del estado del arte de los diferentes enfoques y técnicas de verificación de transformaciones de modelos empleados para MDD. Se analizan las principales caracterÃsticas de los enfoques existentes, a saber: basado en casos de prueba, model checking y métodos deductivos. Asà mismo se estudian las diferentes técnicas existentes para cada enfoque y se presentan las herramientas utilizadas en la bibliografÃa, ejemplificando su uso
