The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform
Houari Sahraoui - One of the best experts on this subject based on the ideXlab platform.
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Recovering software Product line architecture of a family of object-oriented Product variants
Journal of Systems and Software, 2017Co-Authors: Anas Shatnawi, Abdelhak-djamel Seriai, Houari SahraouiAbstract:Software Product Line Engineering (SPLE) aims at applying a pre-planned systematic reuse of large-grained software artifacts to increase the software Productivity and reduce the development cost. The idea of SPLE is to analyze the business domain of a family of Products to identify the common and the variable parts between the Products. However, it is common for companies to develop, in an ad-hoc manner (e.g. clone and own), a set of Products that share common services and differ in terms of others. Thus, many contributions are proposed to re-engineer existing Product variants to a software Product line. Nevertheless, these contributions are mostly focused on managing the variability at the requirement level. Few contributions address the variability at the architectural level despite its major importance. Starting from this observation, we propose an approach to reverse engineer the architecture of a set of Product variants. Our goal is to identify variability and dependencies among architectural-element variants. Our work relies on formal concept analysis to analyze the variability. To validate the proposed approach, we experimented on two families of Open-Source Product variants; Mobile Media and Health Watcher. %The results show that our approach is able to identify the architectural variability and the dependencies.
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Recovering Architectural Variability of a Family of Product Variants
2015Co-Authors: Anas Shatnawi, Abdelhak-djamel Seriai, Houari SahraouiAbstract:A Software Product Line (SPL) aims at applying a pre-planned systematic reuse of large-grained software artifacts to increase the software Productivity and reduce the development cost. The idea of SPL is to analyze the business domain of a family of Products to identify the common and the variable parts between the Products. However, it is common for companies to develop, in an ad-hoc manner (e.g. clone and own), a set of Products that share common functionalities and differ in terms of others. Thus, many recent research contributions are proposed to re-engineer existing Product variants to a SPL. Nevertheless, these contributions are mostly focused on managing the variability at the requirement level. Very few contributions address the variability at the architectural level despite its major importance. Starting from this observation, we propose, in this paper, an approach to reverse engineer the architecture of a set of Product variants. Our goal is to identify the variability and dependencies among architectural-element variants at the architectural level. Our work relies on Formal Concept Analysis (FCA) to analyze the variability. To validate the proposed approach, we experimented on two families of Open-Source Product variants; Mobile Media and Health Watcher. The results show that our approach is able to identify the architectural variability and the dependencies.
Krzysztof Czarnecki - One of the best experts on this subject based on the ideXlab platform.
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towards predicting feature defects in software Product lines
Feature-oriented software development, 2016Co-Authors: Rodrigo Queiroz, Thorsten Berger, Krzysztof CzarneckiAbstract:Defect-prediction techniques can enhance the quality assurance activities for software systems. For instance, they can be used to predict bugs in Source files or functions. In the context of a software Product line, such techniques could ideally be used for predicting defects in features or combinations of features, which would allow developers to focus quality assurance on the error-prone ones. In this preliminary case study, we investigate how defect prediction models can be used to identify defective features using machine-learning techniques. We adapt process metrics and evaluate and compare three classifiers using an Open-Source Product line. Our results show that the technique can be effective. Our best scenario achieves an accuracy of 73 % for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.
Anas Shatnawi - One of the best experts on this subject based on the ideXlab platform.
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Recovering software Product line architecture of a family of object-oriented Product variants
Journal of Systems and Software, 2017Co-Authors: Anas Shatnawi, Abdelhak-djamel Seriai, Houari SahraouiAbstract:Software Product Line Engineering (SPLE) aims at applying a pre-planned systematic reuse of large-grained software artifacts to increase the software Productivity and reduce the development cost. The idea of SPLE is to analyze the business domain of a family of Products to identify the common and the variable parts between the Products. However, it is common for companies to develop, in an ad-hoc manner (e.g. clone and own), a set of Products that share common services and differ in terms of others. Thus, many contributions are proposed to re-engineer existing Product variants to a software Product line. Nevertheless, these contributions are mostly focused on managing the variability at the requirement level. Few contributions address the variability at the architectural level despite its major importance. Starting from this observation, we propose an approach to reverse engineer the architecture of a set of Product variants. Our goal is to identify variability and dependencies among architectural-element variants. Our work relies on formal concept analysis to analyze the variability. To validate the proposed approach, we experimented on two families of Open-Source Product variants; Mobile Media and Health Watcher. %The results show that our approach is able to identify the architectural variability and the dependencies.
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Recovering Architectural Variability of a Family of Product Variants
2015Co-Authors: Anas Shatnawi, Abdelhak-djamel Seriai, Houari SahraouiAbstract:A Software Product Line (SPL) aims at applying a pre-planned systematic reuse of large-grained software artifacts to increase the software Productivity and reduce the development cost. The idea of SPL is to analyze the business domain of a family of Products to identify the common and the variable parts between the Products. However, it is common for companies to develop, in an ad-hoc manner (e.g. clone and own), a set of Products that share common functionalities and differ in terms of others. Thus, many recent research contributions are proposed to re-engineer existing Product variants to a SPL. Nevertheless, these contributions are mostly focused on managing the variability at the requirement level. Very few contributions address the variability at the architectural level despite its major importance. Starting from this observation, we propose, in this paper, an approach to reverse engineer the architecture of a set of Product variants. Our goal is to identify the variability and dependencies among architectural-element variants at the architectural level. Our work relies on Formal Concept Analysis (FCA) to analyze the variability. To validate the proposed approach, we experimented on two families of Open-Source Product variants; Mobile Media and Health Watcher. The results show that our approach is able to identify the architectural variability and the dependencies.
Jaume Nin-guerrero - One of the best experts on this subject based on the ideXlab platform.
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An Open Source Product-oriented LTE network simulator based on ns-3
Proceedings of the 14th ACM international conference on Modeling analysis and simulation of wireless and mobile systems - MSWiM '11, 2011Co-Authors: Nicola Baldo, Marco Miozzo, Manuel Requena-esteso, Jaume Nin-guerreroAbstract:In this paper we present a new simulation module for ns-3 aimed at the simulation of LTE networks. This module has been designed with a Product-oriented perspective in order to allow LTE equipment manufacturers to test RRM/SON algorithms in a simulation environment before they are de- ployed in the field. First, we describe the design of our simu- lation module, highlighting its novel aspects. Subsequently, we discuss the testing methodology that we adopted to val- idate its output. Finally, we present some experimental re- sult to assess its performance in terms of execution time and memory usage.
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MSWiM - An Open Source Product-oriented LTE network simulator based on ns-3
Proceedings of the 14th ACM international conference on Modeling analysis and simulation of wireless and mobile systems - MSWiM '11, 2011Co-Authors: Nicola Baldo, Marco Miozzo, Manuel Requena-esteso, Jaume Nin-guerreroAbstract:In this paper we present a new simulation module for ns-3 aimed at the simulation of LTE networks. This module has been designed with a Product-oriented perspective in order to allow LTE equipment manufacturers to test RRM/SON algorithms in a simulation environment before they are deployed in the field. First, we describe the design of our simulation module, highlighting its novel aspects. Subsequently, we discuss the testing methodology that we adopted to validate its output. Finally, we present some experimental result to assess its performance in terms of execution time and memory usage.
Rodrigo Queiroz - One of the best experts on this subject based on the ideXlab platform.
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towards predicting feature defects in software Product lines
Feature-oriented software development, 2016Co-Authors: Rodrigo Queiroz, Thorsten Berger, Krzysztof CzarneckiAbstract:Defect-prediction techniques can enhance the quality assurance activities for software systems. For instance, they can be used to predict bugs in Source files or functions. In the context of a software Product line, such techniques could ideally be used for predicting defects in features or combinations of features, which would allow developers to focus quality assurance on the error-prone ones. In this preliminary case study, we investigate how defect prediction models can be used to identify defective features using machine-learning techniques. We adapt process metrics and evaluate and compare three classifiers using an Open-Source Product line. Our results show that the technique can be effective. Our best scenario achieves an accuracy of 73 % for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.
