Identifying Counterexamples Without Variability in Software Product Line Model Checking

Product detection based on state abstraction technologies in the software product line(SPL)is more complex when compared to a single system.This variability constitutes a new complexity,and the counterexample may be valid for some products but spurious for others.In this paper,we found that spurious products are primarily due to the failure states,which correspond to the spurious counterexamples.The violated products correspond to the real counterexamples.Hence,identifying counterexamples is a critical problem in detecting violated products.In our approach,we obtain the violated products through the genuine counterexamples,which have no failure state,to avoid the tedious computation of identifying spurious products dealt with by the existing algorithm.This can be executed in parallel to improve the efficiency further.Experimental results showthat our approach performswell,varying with the growth of the system scale.By analyzing counterexamples in the abstract model,we observed that spurious products occur in the failure state.The approach helps in identifying whether a counterexample is spurious or genuine.The approach also helps to check whether a failure state exists in the counterexample.The performance evaluation shows that the proposed approach helps significantly in improving the efficiency of abstraction-based SPL model checking.

Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line

Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.

Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability.The demand for energy and resource require-ments is rising when producing hardware and software units.Literature study reveals that many studies focused on green hardware;however,limited efforts were made in the greenness of software products.Green software products are necessary to solve the issues and problems related to the long-term use of soft-ware,especially from a sustainability perspective.Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment,the mentioned benefits will not be attained.Currently,there are not enough works to address this problem,and the green status of software products is uncertain and unsure.This paper aims to identify the green measurements based on sustainable dimensions in a software product.The second objective is to reveal the relationships between the elements and measurements through empirical study.The study is conducted in two phases.Thefirst phase is the theoretical phase,where the main components,measurements and practices that influence the sustainability of a software product are identified.The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achiev-ing green software products.This study has revealed seven green measurements of software product:Productivity,Usability,Cost Reduction,Employee Support,Energy Efficiency,Resource Efficiency and Tool Support.The relationships are statistically significant,with a significance level of less than 0.01(p=0.000).Thus,the hypothesised relationships were all accepted.The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.

Online Fashion Retailer Bonobos Selects Gerber Technology's YuniquePLM^(TM) Industry-Tailored Software to Support Rapid Growth and Fast-Paced Product Innovation

Tolland,Connecticut,USA-Gerber Technology,a business unit of Gerber Scientific, Inc.(NYSE:GRB) and a world leader in automated CAD/CAM and PLM solutions for the apparel and flexible materials industry,announces that fast-growing internet retailer Bonobos,Inc. has selected the YuniquePLM product

Assessment of Trends in the Development of the It Market

The demand for information technology in a new way forced us to reconsider our views on the state of the information and communication technology market. The analysis of the trend in the development of the information space is necessary for the formation of a scenario for the formation of a stable information and communication industry in the hotel state. The article proposes the structure of the IT market, defines the role of its main parts and concludes about their impact on the further development of the IT industry. A brief overview of the components of the modern IT market is presented. The greatest influence is provided by cloud solutions, the Internet of Things market and digital products in the field of artificial intelligence. Observation of changes in the IT market allows us to predict further events related to digitalization. In conclusion, possible ways of developing the IT market are given.

STPSO: Optimal Configuration for Cloud Environments

With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self-Tuning Particle Swarm Optimization approach(called STPSO) is proposed to configure the cloud environment. STPSO can automatically adjust the arbitrary configuration to a valid configuration. To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.

Maintain Optimal Configurations for Large Configurable Systems Using Multi-Objective Optimization

To improve the maintenance and quality of software product lines,efficient configurations techniques have been proposed.Nevertheless,due to the complexity of derived and configured products in a product line,the configuration process of the software product line(SPL)becomes timeconsuming and costly.Each product line consists of a various number of feature models that need to be tested.The different approaches have been presented by Search-based software engineering(SBSE)to resolve the software engineering issues into computational solutions using some metaheuristic approach.Hence,multiobjective evolutionary algorithms help to optimize the configuration process of SPL.In this paper,different multi-objective Evolutionary Algorithms like Non-Dominated Sorting Genetic algorithms II(NSGA-II)and NSGA-III and Indicator based Evolutionary Algorithm(IBEA)are applied to different feature models to generate optimal results for large configurable.The proposed approach is also used to generate the optimized test suites with the help of different multi-objective Evolutionary Algorithms(MOEAs).

A Feature Model Based Framework for Refactoring Software Product Line Architecture

Software product line （SPL） is an approach used to develop a range of software products with a high degree of similarity. In this approach, a feature model is usually used to keep track of similarities and differences. Over time, as modifications are made to the SPL, inconsistencies with the feature model could arise. The first approach to dealing with these inconsistencies is refactoring. Refactoring consists of small steps which, when accumulated, may lead to large-scale changes in the SPL, resulting in features being added to or eliminated from the SPL. In this paper, we propose a framework for refactoring SPLs, which helps keep SPLs consistent with the feature model. After some introductory remarks, we describe a formal model for representing the feature model. We express various refactoring patterns applicable to the feature model and the SPL formally, and then introduce an algorithm for finding them in the SPL. In the end, we use a real-world case study of an SPL to illustrate the applicability of the framework introduced in the paper.

Collaborative software design in an SOA environment

Based on the current Service-Oriented Architecture （SOA）, this paper proposes a new collaborative software design methodology in an SOA Environment： the Global Software Enterprise （GSE）. The current SOA is producer-centric, in which the service providers publish services that they produce and let the consumers to search avail- able services to compose their applications. GSE is build on top of Consumer-Centric SOA （CCSOA）, in which the application builders publish their application requirements for the service providers to follow when producing or customizing services to support the application. This new methodology reduces the workload and improves the application description capability of the service consumers. It also extends the capacity of design and code sharing, and thus further improves the software productivity. This paper presents the concepts, architecture, enabling techniques, and illustrative examples of collaborative software design in GSE.

Feature-Oriented Nonfunctional Requirement Analysis for Software Product Line

Domain analysis in software product line （SPL） development provides a basis for core assets design and imple- mentation by a systematic and comprehensive commonality/variability analysis. In feature-oriented SPL methods, products of the domain analysis are domain feature models and corresponding feature decision models to facilitate application-oriented customization. As in requirement analysis for a single system, the domain analysis in the SPL development should con- sider both flmctional and nonfunctional domain requirements. However, the nonfunctional requirements （NFRs） are often neglected in the existing domain analysis methods. In this paper, we propose a context-based method of the NFR analysis for the SPL development. In the method, NFRs are materialized by connecting nonfunctional goals with real-world context, thus NFR elicitation and variability analysis can be performed by context analysis for the whole domain with the assistance of NFR templates and NFR graphs. After the variability analysis, our method integrates both functional and nonfunc- tional perspectives by incorporating the nonfunctional goals and operationalizations into an initial functional feature model. NFR-related constraints are also elicited and integrated. Finally, a decision model with both functional and nonfunctional perspectives is constructed to facilitate application-oriented feature model customization. A computer-aided grading system （CAGS） product line is employed to demonstrate the method throughout the paper.

Efficient software product-line model checking using induction and a SAT solver

Software product line （SPL） engineering is increasingly being adopted in safety-critical systems. It is highly desirable to rigorously show that these systems are designed correctly. However, formal analysis for SPLs is more difficult than for single systems because an SPL may contain a large number of individual systems. In this paper, we propose an efficient model-checking technique for SPLs using induction and a SAT （Boolean satisfiability problem） solver. We show how an induction-based verification method can be adapted to the SPLs, with the help of a SAT solver. To combat the state space explosion problem, a novel technique that exploits the distinguishing characteristics of SPLs, called feature cube enlargement, is proposed to reduce the verification efforts. The incremental SAT mechanism is applied to further improve the efficiency. The correctness of our technique is proved. Experimental results show dramatic improvement of our technique over the existing binary decision diagram （BDD）-based techniques.

VARIABILITY MODELING TO DEVELOP FLEXIBLE SERVICE-ORIENTED APPLICATIONS

To cope with requirement changes flexibly and rapidly,the existing component-based paradigm is being evolved into a service-oriented computing paradigm.The main characteristic of the service-oriented computing paradigm is that service-oriented applications are developed as loosely coupled services that reflect business concerns.This paradigm also promotes business agility,facilitating quick reactions to business changes.Therefore,to enhance and support the benefits of the service-oriented computing paradigm,we must consider how to improve flexibility and reusability during the development of service-oriented applications.We propose the variability modeling approach to specify and control the common and distinguishing characteristics of service-oriented applications.That is,the key concepts of product-line technology can be used to make service-oriented applications more flexible and reusable.This paper describes variability modeling at two levels;the composition level and the specification level.At the composition level,we describe the variability of composition and the flow of domain services that fulfill business processes.At the specification level,we present a domain service that is an abstract service with variability.The use of our systematic variability modeling approach can greatly increase the flexibility,applicability,and reusability of service-oriented applications.

Automating identification of services and their variability for product lines using NSGA-II

Architecture-level business services are identi- fied based on business processes; and likewise, in service- oriented product lines, identifying the domain architecture- level business services and their variability is preferred to be based on business processes and their variability. Identifica- tion of business services for a product line satisfying a set of given design metrics （such as cohesion and coupling） is ex- tremely difficult for a domain architect, since there are many product configurations for which the services must be proper at the same time. This means that the identified services must have proper values for n metrics in m different configura- tions at the same time. The problem becomes more serious when there are high degrees of variability and complexity embedded in the business processes that are the basis for ser- vice identification. We contribute to solve the multi-objective optimization problem of identifying business services for a product line by partitioning the graph of a business process variability model utilizing Non-dominated Sorting Genetic Algorithm-II. The service specification is achieved based on the results of the partitioning. The variability of the services is then determined in terms of mandatory and optional services as well as variability relationships, which are all represented in a Service Variability Model. The method was empirically evaluated through experimentation, and showed proper lev- els of reusability and variability. Furthermore, the resulting models were fully consistent.