A Brief Survey on Product Derivation Methods in Software Product Line

Product Derivation represents one of the main challenges that a Software Product Line (SPL) faces. Deriving individual products from shared software assets is a time-consuming and an expensive activity. Until now, only few works addressed, in a limited context, a partial evaluation of a reduced number of proposed derivation approaches. The main objective of such studies was the comparison of a proposed approach regarding two or three approaches. The purpose of the study reported in this paper is to set up a framework oriented to evaluate and compare existing SPL derivation approaches. The proposed framework uses a number of criteria which help understanding the capabilities and highlight the strength and the weakness of each SPL derivation approach

Software industry experiments: a systematic literature review

There is no specialized survey of experiments conducted in the software industry. Goal: Identify the major features of software industry experiments, such as time distribution, independent and dependent variables, subject types, design types and challenges. Method: Systematic literature review, taking the form of a scoping study. Results: We have identified 10 experiments and five quasi-experiments up to July 2012. Most were run as of 2003. The main features of these studies are that they test technologies related to quality and management and analyse outcomes related to effectiveness and effort. Most experiments have a factorial design. The major challenges faced by experimenters are to minimize the cost of running the experiment for the company and to schedule the experiment so as not to interfere with production processes

Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.Fil: Buccella, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; ArgentinaFil: Pol'la, Matias Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; ArgentinaFil: Cechich, Susana Alejandra. Universidad Nacional del Comahue. Facultad de Informatica; Argentin

Hephaestus-PL : uma linha de produtos de ferramentas para linha de produtos de software

Dissertação (mestrado)—Universidade de Brasília, Departamento de Ciência da Computação, 2012.Suporte ferramental e essencial para a Engenharia de Aplica ção em Linhas de Produto de Software (LPS). Apesar de uma variedade de ferramentas existentes, a maioria delas não apresenta suporte adequado a con gurabilidade e exibilidade. Assim sendo, e dif cil para elas serem aplicadas em diferentes contextos, por exemplo, endere car variabilidade em diferentes combina ções de artefatos e permitir a inserção e o gerenciamento de variabilidades de novos artefatos de diferentes dom nios. Para abordar esta questão, e necessário explorar sistematicamente a comunalidade e, adequadamente, gerenciar a variabilidade de tais ferramentas. Nesse sentido, realizamos uma an alise comparativa de t ecnicas de gerenciamento de variabilidades para o desenvolvimento de ferramentas de LPS no contexto da ferramenta Hephaestus. A an alise revela que duas t ecnicas, uma anotativa e outra transformacional, são as mais adequadas ao gerenciamento de variabilidades em Hephaestus, e que a sua combina ção e uma estrat egia viável para melhorar esse gerenciamento. Além disso, apresentamos a an álise, projeto e implementa ção do dom ínio e um processo que suporta a evolu cão de Hephaestus-PL, uma linha de produtos de ferramentas de linha de produtos de software onde o gerenciamento de variabilidades foi implementado por abordagem transformacional usando opera c~oes de metaprogramação. Hephaestus-PL suporta um processo que permite a instanciaç~~ao de ferramentas de linha de produtos modelando a variabilidade em novos e em qualquer combina ção de artefatos, e foi desenvolvida por bootstrapping de versões da ferramenta Hephaestus. Este processo suporta a aborda- gem reativa e a exibilidade para introduzir novos ativos aumentando a con gurabilidade de Hephaestus-PL e permitindo a gera cão de diferentes instâncias de Hephaestus-PL. Uma avalia cão da solu ção proposta revela que a mesma melhorou a con gurabilidade e exibilidade quando comparamos com as evolu cões anteriores de Hephaestus. ______________________________________________________________________________ ABSTRACTTool support is essential for application engineering in software product lines. Despite a myriad of existing tools, most still lack adequate support for con gurability and exibility, so that it is hard for them to be applied in di erent contexts, e.g., addressing variability in an arbitrary combination of di erent artifacts and introducing and managing variabil- ity in new artifacts. Addressing this issue requires systematically exploring underlying commonality and adequately managing variability of such tools. Accordingly, we have conducted a comparative analysis of variability management techniques for SPL tool development in the context of the SPL Hephaestus tool. The analysis reveals that two techniques, one annotative and another transformational, are most suitable to variability management in Hephaestus, and that their combination is a feasible strategy to improve such management. Furthermore, we present domain analysis, design, implementation, and a supporting process for extending Hephaestus-PL, a software product line of software product line tools whose variability management was implemented by transformational approach us- ing metaprogramming operations. Hephaestus-PL is supported by a process allowing instantiating product line tools for modeling variability in new and in any combination of artifacts, and has been developed by bootstrapping previous versions of the Hephaestus tool. This process supports the reactive approach and exibility to add new assets increas- ing the con gurability of Hephaestus-PL and reaching the goal of enabling the generation of di erent instances of Hephaestus-PL. An assessment of the proposed solution reveals that it has improved con gurability and exibility when compared to previous evolution of Hephaestus

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance

Industrial validation of COVAMOF

COVAMOF is a variability management framework for product families that was developed to reduce the number of iterations required during product derivation and to reduce the dependency on experts. In this paper, we present the results of an experiment with COVAMOF in industry. The results show that with COVAMOF, engineers that are not involved in the product family were now capable of deriving the products in 100% of the cases, compared to 29% of the cases without COVAMOF. For experts, the use of COVAMOF reduced the number of iterations by 42%, and the total derivation time by 38%. (c) 2007 Elsevier Inc. All rights reserved