=Paper=
{{Paper
|id=Vol-1848/CAiSE2017_Forum_Paper10
|storemode=property
|title=Model Fragment Reuse Driven by Requirements
|pdfUrl=https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper10.pdf
|volume=Vol-1848
|authors=Raúl Lapeña,Jaime Font,Carlos Cetina,Óscar Pastor
|dblpUrl=https://dblp.org/rec/conf/caise/LapenaFCP17
}}
==Model Fragment Reuse Driven by Requirements==
https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper10.pdf
Model Fragment Reuse Driven by Requirements
Raúl Lapeña1 , Jaime Font1 , Carlos Cetina1 , and Óscar Pastor2
1
SVIT Research Group, Universidad San Jorge
Autovía A-23 Zaragoza-Huesca Km.299
50830 Villanueva de Gállego (Zaragoza), Spain
{rlapena,jfont,ccetina}@usj.es
2
Centro de Investigación en Métodos de Producción de Software
Universitat Politècnica de València
Camino de Vera, s/n, 46022 Valencia, Spain
opastor@pros.upv.es
Abstract. Clone-and-Own is a common practice in families of software
products, where parts from legacy products are reused in new devel-
opments. In industrial scenarios, CAO consumes high amounts of time
and effort, not guaranteeing good results. We propose a novel approach,
Computer Assisted CAO for Models (CACAO4M), that uses a Multi-
Objective Evolutionary Algorithm (MOEA) with two objectives (Model
Fragment Similitude, and Model Fragment Understandability) to rank
relevant model fragments for reuse. We evaluated our approach in the
industrial domain of train control software. Our approach outperforms
the results of a baseline that uses only the Model Fragment Similitude
metric, which encourages us to further research in this direction.
Keywords: Software Reuse, Feature Location, Model Driven Develop-
ment
1 Introduction
Clone-And-Own (CAO) [1] is a common practice in the development of new
products, consisting of adapting elements from legacy products in new product
implementations. Reuse enables faster software development and easier tracking
of projects, and helps maintain the development style and conventions consistent
between products. In practice, CAO is carried out manually, relying on develop-
ers’ knowledge of the family. In industrial scenarios, engineers tasked with new
developments often lack knowledge over the entirety of the family, making CAO
consume high amounts of time and effort, without guaranteeing good results.
This paper presents Computer Assisted Clone-And-Own for Models (CA-
CAO4M), a novel approach for software families where products are developed
through Model-Driven Development (MDD). The approach leverages the Multi-
Objective Evolutionary Algorithm (MOEA) [2] technique to rank relevant model
fragments for the requirements of a new development with two objectives: (1)
the similitude of model fragments to the provided requirement, and (2) the un-
derstandability of model fragments from the perspective of a software engineer.
X. Franch, J. Ralyté, R. Matulevičius, C. Salinesi, and R. Wieringa (Eds.):
CAiSE 2017 Forum and Doctoral Consortium Papers, pp. 73-80, 2017.
Copyright 2017 for this paper by its authors. Copying permitted for private and academic purposes.
The results of our approach are evaluated in the domain of train control soft-
ware with our industrial partner, CAF (http://www.caf.net/en), a worldwide
provider of railway solutions, and compared against those of a baseline that takes
in account only the similitude of model fragments to the provided requirement.
The results of our approach improve those of the baseline, providing engineers
with model fragments that are applicable to the problem requirement and easier
to understand than those of the baseline, encouraging further research.
Through our work, Section 2 presents the background, Section 3 details our
approach, Section 4 evaluates our approach, Section 5 gathers the related works,
and Section 6 concludes the paper.
2 Background
This section presents the Train Control and Management Language (TCML)
that formalizes the products from our industrial partner. It has the expressive-
ness required to (1) describe the interaction between train equipment, and (2)
specify non-functional aspects. We present an equipment-focused simplified sub-
set of TCML, along with a running example.
Product Model Product Model Fragment
Pantograph 1 Pantograph 2 Pantograph 1 Pantograph 2
Circuit Circuit Circuit Circuit
Breaker 1 Breaker 2 Breaker 1 Breaker 2
Converter 1 Converter 2 Converter 1 Converter 2
Circuit Circuit
Breaker 3 Breaker 3
HVAC PA CCTV HVAC PA CCTV
TCML Syntax Model Fragment
High Voltage Voltage Consumer
Contactors
Equipment Converters Equipment
Fig. 1. Example of TCML model and model fragment
Fig. 1 depicts a real-world example model. The right part of Fig. 1 shows an
example model fragment that realizes the "converter assistance" requirement,
which allows the passing of current from one converter to equipment assigned
to its peer to cover overload or failure. To formalize the model fragments used
by CACAO4M, we use the Common Variability Language (CVL) [3], which
defines variants of a base model by replacing variable parts with alternative
model replacements found in a library.
74
3 Approach
Fig. 2 presents an overview of CACAO4M. As inputs, we use one requirement
for a new product in the family, and the models that implement the legacy
products in the family. Our approach runs in three steps: (1) Initialization,
(2) application of Genetic Operations, and (3) Fitness Function assessing.
The last two steps of the approach are repeated until the solution converges to a
certain stop condition. When this occurs, the genetic algorithm provides a model
fragment list, ranked according to the objectives, for the requirement.
New product
Legacy Models
requirement
CACAO4M Step 1 - Initialization
Initial Model
Fragment Population
Step 2 - Genetic Operations
Evaluated Model Model Fragment
Fragment Population Population
Step 3 - Fitness Function
3.1 - Model 3.2 - Model
Similitude Understandability
No
Converges?
Model Fragment
No Yes Ranking
Fig. 2. Approach Overview
3.1 Initialization & Genetic Operations
The first step of our approach generates an initial collection of model fragments,
by randomly extracting parts of the legacy models.
75
The second step of our approach generates a set of model fragments that
could realize the requirement. The generation of new model fragments is done
by applying a set of three genetic operators, adapted to work over model frag-
ments: selection of parents, crossover, and mutation. The selection operator
picks the best candidates from the population as input for the rest of operators.
We follow the wheel selection mechanism [4], where each model fragment from
the population has a probability of being selected proportional to its fitness
score. The crossover operation enables the creation of a new individual by
combining the genetic material from two parent model fragments. The muta-
tion operator is used to imitate the mutations that randomly occur in nature
when new individuals are born. The operations are taken from [5] and [6] re-
spectively, where their application to models is detailed.
3.2 Model Fragment Fitness
The third step of the approach assesses each of the candidate model fragments,
ranking them according to a fitness function. Our approach presents a fitness
function based on two objectives: (1) the degree of similitude of the model frag-
ment to the requirement, and (2) the understandability of the model fragment.
3.2.1 Model Fragment Similitude
To assess the relevance of each model fragment with relation to the provided
requirement, we apply Latent Semantic Indexing (LSI) [7]. LSI constructs vector
representations of a query and a corpus of text documents by encoding them as
a term-by-document co-occurrence matrix. In our approach, terms are keywords
extracted from requirements through natural language processing techniques, the
documents are generated from the model fragments by extracting the terms that
correspond to the elements that conform them, and the query is the provided
requirement. Once the matrix is built, it is normalized and decomposed into
a set of vectors using a matrix factorization technique called Singular Value
Decomposition (SVD) [7]. The similarity degree between the query and each
document is calculated through the cosine between the vectors that represent
them. Fig. 3 shows an example of co-occurrence matrix, taken from our approach.
Fig. 3 also shows the result of applying the SVD technique to the matrix, and
the scores associated to each model fragment.
Documents Query Singular Value Decomposition Score
MF1 MF2 … MFN Re quirement Model Fragment
MFN similitude scores
PANT O 0 2 … 2 1 MF2 = 0.93
Keywords
MF2
CIRCUIT
0 2 … 5 2 Q MFN = 0.24
BREAKER
DOOR 3 0 … 1 1 MF1 …
… … … … … … MF1 = -0.87
Fig. 3. LSI example
76
3.2.2 Model Fragment Understandability
In order to measure the Understandability of a TCML model fragment, we mea-
sure its size by accounting the amount of lines, shapes, and labels that appear
in the model fragment. As an example, we highlight the calculations for the
model on Fig. 1. To compute the Understandability metric, we take in account
the number of lines (9) and the number of shapes (10), for a total of 19 model
elements.
4 Evaluation
This section evaluates our approach by applying it to a case study from our
industrial partner consisting of 23 trains, each one having an associated require-
ments specification document, and an associated model. Requirements from the
document are implemented through model fragments in the model. The docu-
ments specify, on average, around 420 requirements each. The models comprise,
on average, around 1200 elements each.
4.1 Experimental Setup
Fig. 4 shows the steps followed to evaluate our approach. First, roles are assigned
to products in the product family. One product acts as the new product and
the rest act as legacy products. The models of the products that act as legacy
products, and one requirement of the product that acts as the new product are
used to perform CACAO4M, while the model fragment that implements the
latter is kept apart to be used as an oracle. Therefore, in order for a product to
act as an oracle, it is necessary to have the mapping between its requirements
and the model fragments that implement each of them. From the 23 products
in the family, the mapping is available for 4. These products are the ones that
can be used as oracles in our family.
Legacy Models Model Fragments
CACAO4M
Product Role Products Ranking
Family Assignment New Requirement
Product Oracle Comparison
Model
Fragment Confusion Matrix
Precision & Recall
Fig. 4. Evaluation Steps
77
Then, CACAO4M performs the steps described in our approach to provide
a model fragment ranking for the requirement of the new product. We carry out
the genetic algorithm inside CACAO4M, weighing the two metrics (Model Simil-
itude and Model Understandability) as 90% - 10%. We also apply CACAO4M
with a 100% - 0% weighing, to simulate a Single Objective Evolutionary Algo-
rithm (SOEA) where only Model Similitude is taken in account. The SOEA is
considered as the baseline against which the results of the MOEA are compared.
Finally, the first model fragment in the ranking for each result is compared
with the oracle model fragment, in order to obtain a confusion matrix. A confu-
sion matrix is a table used to describe the performance of a classification model
(in this case, our algorithms) on a set of test data (the resulting model frag-
ments) for which the true values are known (from the oracle). Each solution
outputted by the algorithm is a model fragment composed of a subset of the
model elements that are part of the product model (where the requirement is
being located). Since the granularity is at the level of model elements, each model
element presence or absence is considered as a classification.
The confusion matrix distinguishes between the predicted values and the real
values classifying them into four categories: (1) True Positive (TP): predicted
true - real true; (2) False Positive (FP): predicted true - real false; (3) True
Negative (TN): predicted false - real false; and (4) False Negative (FN):
predicted false - real true. The evaluated performance metrics are:
(1) Precision: number of elements from the solution that are correct according
to the ground truth, expressed as
TP
P recision =
TP + FP
(2) Recall: number of elements of the solution retrieved by the proposed solu-
tion, expressed as
TP
Recall =
TP + FN
(3) F-measure: harmonic mean of precision and recall, expressed as
2 ∗ TP
F − measure =
2 ∗ TP + FP + FN
We perform our evaluation separately for every requirement in an oracle, and
for all the 4 possible oracles individually.
4.2 Results
The LSI 90% & U 10% (MOEA) achieves the best results, providing a mean
precision value of 55.34%, a recall value of 49.98%, and a combined F-measure
of 52.52%; while the LSI 100% & U 0% (SOEA) achieves a mean precision value
of 53.21%, a recall value of 49.73%, and a combined F-measure of 51.41%.
78
5 Related Work
Feature location approaches in a product family such as the one presented in [8]
center their efforts in finding the code that implements a feature between the
different products by combining techniques such as FCA and LSI. We are not
interested in the code representation of a feature in the family, but in locating
the most relevant model fragments that implement a requirement.
Works as [9] focus on the location of features over models by comparing the
models with each other to formalize the variability among them in the form of
a Software Product Line. We do not locate features, but model fragments that
implement requirements, and our goal is not to formalize variability, but to help
engineers develop requirements through model fragment Clone-And-Own.
Font et al. [5] use a SOEA to locate features among a family of models in
the form of a variation point. Their approach is refined in [6], where a SOEA
is used to find sets of suitable feature realizations. The presented approach, in
contrast, locates model fragments that are relevant for the development of a
single requirement. The presented approach also differs from [5] and [6] both
technique and metrics, by using a MOEA, with a fitness function that combines
Model Similitude and Model Understandability.
In [10], Lapeña et. al use POS Tagging in combination with an adapted two-
step LSI to obtain rankings of methods for all the requirements of a new product
in a product family. In the presented work, we obtain only one ranking for one
requirement on demand. Plus, this approach uses a MOEA, against the modified
LSI in [10]. Finally, the scope of this work is centered around finding models that
can be used to implement a particular requirement, not finding relevant code for
requirements implementation.
6 Conclusions
Clone-And-Own (CAO) is a common practice in the development of new prod-
ucts in families of software products. In practice, it is carried out manually and
relies on human factors, consuming high amounts of time and effort without
guaranteeing good results. This paper presents Computer Assisted Clone-And-
Own for Models (CACAO4M), a novel approach that leverages a MOEA to
rank relevant model fragments for the development of particular requirements
for a new product. CACAO4M assesses the similitude of model fragments to the
provided requirement, and their understandability. Through our approach, we
aim to prioritize the model fragments that are easier to understand from the
perspective of a software engineer. Our MOEA approach is evaluated against a
SOEA baseline that takes in account only model fragment similitude, outper-
forming the baseline in every performance indicator. Model Understandability
provides a way of locating model fragments that are applicable to the problem
requirement and easier to understand than those retrieved by the SOEA. Results
encourage us to work further in this direction.
79
Acknowledgments
This work has been partially supported by the Ministry of Economy and Com-
petitiveness (MINECO) through the Spanish National R+D+i Plan and ERDF
funds under the project Model-Driven Variability Extraction for Software Prod-
uct Line Adoption (TIN2015-64397-R).
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