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.

ZTE Showcases LTE Product Line in Germany

On November 26, 2009, ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, was showcasing its Long Term Evolution (LTE) products and solutions to network

A Product Line Approach to Design an Embedded Web System for Healthcare

With the recent advances in mobile technology and wireless network technology, embedded systems are being widely used in modem society today. Particularly, a home healthcare system is a networked embedded system where the main functions are to control the disease processes and to help patients maintain their independence and maximum level of function within their own homes and communities. It seems to be self-evident to design a system that would support both patients and their healthcare providers in the process of treatment. Nevertheless, little work in integrating embedded devices with intemet for the support of patients have been done to date. In this paper, we show how to design a healthcare system for supporting the management of the conditions of patients with chronic diseases. This system is built around wireless networked embedded devices, and integrates the intemet technology for telemonitoring the patient＇s health and notifying of doctors if emergency action is required. Also, patients themselves may specify personal alerts for condition-related issues.

Investigation of Tools, Techniques and Languages for Model Driven Software Product Lines (SPL): A Systematic Literature Review

Software development is a complex and difficult task that requires the investment of sufficient resources and carries major risk of failure. Model Driven Engineering (MDE) focuses on creating software models and automating code generation from the models. Model Driven Software Development (MDSD) offers significantly more effective approaches. These approaches improve the way of building software. Model driven approaches partially increase developer productivity, decrease the cost of software construction, improve software reusability, and make software more maintainable. This paper investigates the methods where Model Driven Software Development is integrated with Software Product Line (SPL). This SLR has been conducted to identify 71 research works published since 2014. We have collected 18 tools, 14 techniques and 17 languages used for MDSD for SPL. We analyze which technique is suitable for SPL. We compare the techniques on the basis of features provided by these tools to understand the better-quality results.

Dynamic Promotional Resource Allocation for Segment Specific and Spectrum Effect of Promotion for a Product Line Incorporating Repeat Purchase Behavior

Promotion is an essential element in the marketing mix. It is used by businesses to inform, influence and persuade customers to adopt the products and services they offer. Without promotion, business would be stagnant and lack substantial growth because the brands would have low visibility in the market. Moreover, today’s vast and assorted markets comprise of customers with different needs and varied behavior. So it is rarely possible for companies to satisfy all customers by treating them alike. Thus there arises a need to divide the market into segments having customers with similar traits/characteristics. After identifying appropriate market segments, firms can design differentiated promotional campaigns for each segment. At the same time there can be a mass market promotional campaign that reaches different segments with a fixed spectrum. Also since promotional effort resources are limited, one must use them judiciously. In this paper, we formulate mathematical programming problem under repeat purchase scenario, which optimally allocates mass promotional effort resources and differentiated promotional effort resources across the segments dynamically in order to maximize the overall sales obtained from multiple products of a product line under budgetary and minimum sales aspiration level constraint on each product under consideration in each segment. The planning horizon is divided into multi periods, the adoption pattern of each product in each segment is observed in every subinterval and accordingly promotional effort allocations are determined for the next period till we reach the end of planning period. The optimization model has been further extended to incorporate minimum aspiration level constraints on total sales for each product under consideration from all the segments taken together. The non linear programming problem so formulated is solved using differential evolution approach. A numerical example has been discussed to illustrate applicability of the model.

Learning to Support Derivation of Adaptable Products in Software Product Lines

Software product line engineering is a large scale development paradigm based on mass production. It consists in building a common platform from which a set of products can be derived. Under the constraints of continuous evolution and costs optimization, the derivation process must be able to answer customers’ requirements and provide adequate products in a short time without defects. However, this purpose cannot be achieved if the customer must wait for the change is implemented in the common platform. In this paper, we describe our work which proposes a framework to manage derivation of adaptable products. An adaptable product is obtained by deriving the most similar product from the common platform and changing it to support the new requirements. The aim of the framework is to give quick response to the customers need while the new requirements are being implemented in the common platform. The framework includes tools for processing natural language requirements, computing the similarity between products on the basis of their requirements, and the product adaptation effort measuring.

Conversion of Object Oriented System into Software Product Line with Delta Modeling Abstract Behavioral Specification

This article contains a system conversion from object oriented design into Software Product Line (SPL) using delta modeling of Abstract Behavioral Specification (ABS). ABS is a modeling language which targets system with high level of variety and supports SPL development with delta modeling. The case study of this thesis is a digital library system called Library Automation and Digital Archive (LONTAR). Originally, LONTAR only uses SOAP-based web service. With ABS, LONTAR will be converted into SPL and implement another web service called REST. The motivation of this conversion of LONTAR from object oriented into SPL is because it is easier to develop system with ABS than using regular object oriented. Product definition in ABS is relatively easier than creating a new subclass and do customization to make it works well.

An Aspect-Oriented Approach for Use Case Based Modeling of Software Product Lines

Software Product Line Development advocates software reuse by modeling common and variable artefacts separately across members of a family of products. Aspect-Oriented Software Development aims at separation of concerns with “aspects” to increase modularity, reusability, maintainability and ease of evolution. In this paper, we apply an as-pect-oriented use case modeling approach to product line system modeling. A use case specification captures stake-holders concerns as interactions between a system and its actors. We adapt our previous work with the introduction of a “variability” relationship for the expression of variabilities. This relationship is used to model variable and common behaviours across a family of products as use cases. A variability composition mechanism enables building of executa-ble behaviour models for each member of a product line family by integrating common elements with the applicable variable elements.

Quality-Oriented Software Product Line Architecture Design

Software architecture design is a critical step of software development. Currently, there are various design methods available and each is focusing on certain perspective of architecture design. Especially, quality-based methods have received a lot of attentions and have been well developed for single system architecture design. However, the use of quality-based design methods is limited in software product line (SPL) because of the complexity and variabilities existing in SPL architecture. In this paper, we introduce an extra view to the Quality-Driven Architecture Design and quality Analysis (QADA) method, in order to provide a more effective quality-based architecture design framework for SPL. In this framework, the quality attributes of a software system will be taken into account in the early stage of architecture design and the reference architecture of SPL will be elicited based on quality-related consideration.

Data-Centric Approach to Digital Twin Modeling of Production Lines

Digital twin(DT) is a virtual replica of a physical world that has become one of the most important ideas in the manufacturing industry’s digital revolution. DT modeling is a vital issue in building a DT of a production line. In this paper, a method is proposed to address the difficulties of complicated production line business and data heterogeneity. The method focuses on essential data in the production line and creates conceptual and information models based on the ArtiFlow model and AutomationML(AML). Conceptual models are mainly used to describe and analyze the business activities of the production line, and information models describe real production lines in the form of XML files. The proposed modeling approach has been applied to a real-world clothing production line to demonstrate its feasibility and effectiveness.

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.

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.

Installation and Debugging of Auto-control System in Fruit Wine Production Line

[Objective] The aim was to test the controlling effect of cleaning steriliza- tion system, material conveying system, and fermentation jar cooling system with equip- ments of fruit wine production line introduced in this study and its auto-control sys- tem field assembled and debugged. [Method] Based on controlling equipment and setting parameters on the configuration interface, the operation state of the control equipments could be real-time monitored and controlled with the help of configura- tion software. [Result] The result showed that the equipment system could reduce the temperature into 12 ℃ with the error of ＋0.5 ℃within 110 minutes when the fermentation temperature is set at 12 ℃ in real production. [Conclusion] The auto- control system of fruit wine production line was easy to be assembled and de- bugged to meet demands of different fruit wine productions.

Comparison of Efficiencies of Two Production Lines with Buffering Capacities at Common and Separative Allocation

In this paper, the efficiencies of two production lines, one is with commonly, the other is with separately buffer capacities are compared, the result is that the former one is better than the latter

ANALYSIS OF DYNAMIC PRICING SCENARIOS FOR MULTIPLE-GENERATION PRODUCT LINES

In technology-intensive markets, it is a common strategy for companies to develop long-term multiple generation product lines instead of releasing consecutive single products. Even though this strategy is more profitable than sequentially introducing single product generations, it can also result in inter-product line cannibalization. Cannibalization of multiple-generation product lines is a complex problem that needs to be taken into account at the early product line planning stage in order to sustain long-term profitability. In this paper, we propose an agent-based model that can simulate the potential cannibalization scenarios within a multiple-generation product line. We view a multiple-generation product line （MGPL） as complex adaptive system where each product generation in the MGPL adjusts its sales price over time based on the shifts in the market demand. The proposed model provides insights into how various pricing strategies impact the overall lifecycle profitability of MGPL and can be used to assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.

Private Label Positioning and Product Line

This article examines （i） how retailers position private label products, （ii） why private labels are sold in some product categories but not in others, and why some national brand products may have difficulty in accessing retailers＇ shelves, （iii） why some private label products are positioned as ＂premium＂ brands, and （iv） how consumers＇ surplus and total welfare are affected by private labels. We find that private label positioning leads to less differentiation in product category, which structurally changes a retailer＇s product line in return. Consumer welfare and total welfare are lower

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

Multi-objective reconfigurable production line scheduling for smart home appliances

In a typical discrete manufacturing process,a new type of reconfigurable production line is introduced,which aims to help small-and mid-size enterprises to improve machine utilization and reduce production cost.In order to effectively handle the production scheduling problem for the manufacturing system,an improved multi-objective particle swarm optimization algorithm based on Brownian motion(MOPSO-BM)is proposed.Since the existing MOPSO algorithms are easily stuck in the local optimum,the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM.To further strengthen the global search capacity,a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function(GCDF)is included,which helps to maintain an excellent convergence rate of the algorithm.Based on the commonly used indicators generational distance(GD)and hypervolume(HV),we compare the MOPSO-BM with several other latest algorithms on the benchmark functions,and it shows a better overall performance.Furthermore,for a real reconfigurable production line of smart home appliances,three algorithms,namely non-dominated sorting genetic algorithm-II(NSGA-II),decomposition-based MOPSO(dMOPSO)and MOPSO-BM,are applied to tackle the scheduling problem.It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.