Model-to-Code Transformation from Product-Line Architecture Models to AspectJ

Díaz, Jessica; Pérez, Jennifer; Fernández-Sánchez, Carlos; Garbajosa, Juan

doi:10.1109/seaa.2013.11

Cited by 4 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the feature model level, the associations model interactions among feature models; at configuration level, they model links between configurations; at code level, they drive the code generation. To some extent, we are close to the definition of connectors used by Dial et al in their work on transforming an SPL architecture to Aspect-J code [18]. Nevertheless we do not use an approach based on aspects for the generation, especially as we do not only target a composition of components.…”

Section: B Domain-driven Msplmentioning

confidence: 87%

Managing a Software Ecosystem Using a Multiple Software Product Line: A Case Study on Digital Signage Systems

Urli

Blay–Fornarino

Collet

et al. 2014

2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications

View full text Add to dashboard Cite

Abstract-With the advent of Web 2.0, the growth of developer teams and user communities increases the number of software ecosystems: software platforms developed and maintained in a decentralized way by external contributors. As complexity grows, these large software systems become more and more complex to manage and to adapt to specific user needs. In this paper, we report on a case study on the development of a digital signage software system called YourCast. Based on several years experience evolving YourCast from a single system to a mediumscale ecosystem, we show how organizing it as a multiple software product line helps in organizing the software platform, taming some management tasks for a growing community, and giving more capabilities to final users to build their own products.

show abstract

Section: B Domain-driven Msplmentioning

confidence: 87%

Managing a Software Ecosystem Using a Multiple Software Product Line: A Case Study on Digital Signage Systems

Urli

Blay–Fornarino

Collet

et al. 2014

2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications

View full text Add to dashboard Cite

show abstract

“…The authors mentioned [30,31] their work on the evolution, runtime, and execution of SPLs. The configuration of SPLs has been explicitly discussed by other researchers who have mainly focused on cost, user preferences, and feature model constraint rules [32][33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

et al. 2023

View full text Add to dashboard Cite

Currently, software development is more associated with families of configurable software than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking. Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment’s time and financial constraints, these features are rendered unfeasible. Thus, testing subsets of configured products is one approach to solving this issue. To reduce the testing effort and obtain better results, alternative methods for testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately, the CIT method produces unscalable solutions for large SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, should also be considered. In this article, we proposed a search-based software engineering solution using multi-objective evolutionary algorithms (MOEAs). In particular, the research was applied to different types of MOEA method: the Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-dominant Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms were examined in the context of distinct objectives and two quality indicators. The results revealed how the feature model attributes, implementation context, and number of objectives affected the performances of the algorithms.

show abstract

“…Authors have mentioned [30,31] their work on SPLs evolution run time and execution. While the issue of SPLs configuration was discussed formally by other researchers who mainly focus on cost, user preferences, and feature model violations rules [32][33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

Software Product Lines Maintenance using Many Objectives Optimization Techniques

Jamil¹,

Nour²,

Alotaibi³

et al. 2023

Preprint

View full text Add to dashboard Cite

Currently, software development is more associated with families of configurable software rather than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking (SPL). Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment's time and financial constraints, these features are rendered unfeasible. Testing subsets of configured products is thus one approach to solving this issue. In order to reduce testing effort and get better results, alternative methods of testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately CIT method produces unscalable solutions for large size SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, have also been considered. In this article, we have proposed a search-based software engineering solution using multi-objective optimization algorithms (MOEAs). In particular, the research is applied to different types of MOEA methods; Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-Dominated Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms are examined in the context of distinct objectives and two quality indicators. The results revealed how feature model attributes, implementation context and the number of objectives affect the performance of the algorithms.

show abstract

Model-to-Code Transformation from Product-Line Architecture Models to AspectJ

Cited by 4 publications

References 19 publications

Managing a Software Ecosystem Using a Multiple Software Product Line: A Case Study on Digital Signage Systems

Managing a Software Ecosystem Using a Multiple Software Product Line: A Case Study on Digital Signage Systems

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

Software Product Lines Maintenance using Many Objectives Optimization Techniques

Contact Info

Product

Resources

About