Feature Model Configuration Research Articles

International Trade Partner Selection Model and Its Influencing Factors Based on Machine Learning

Iinternational trade partner selection model and its influencing factors based on machine learning represents a significant advancement in global business strategies. By harnessing the capabilities of machine learning algorithms, this model analyzes a multitude of variables such as market trends, economic indicators, and partner attributes to identify optimal trade partners for businesses. Factors such as geographical proximity, market stability, and cultural compatibility are incorporated into the model to provide data-driven insights into partner selection decisions. This paper investigates the application of Stacked Random Field Machine Learning (SRF-ML) in the domain of international trade partner selection. The selection of suitable trade partners is a crucial aspect of global trade operations, influencing the efficiency and success of business ventures. Traditional approaches to partner selection often rely on subjective assessments or simplistic models, which may overlook important factors and lead to suboptimal decisions. In contrast, SRF-ML offers a powerful framework for analyzing complex datasets and making informed predictions about partner suitability. Through the integration of multiple layers of random field models, SRF-ML can effectively capture intricate relationships between various attributes and provide more accurate assessments of partner compatibility. In this paper explore the performance of SRF-ML models across different datasets and scenarios, considering factors such as market size, economic stability, and logistical capability. The results demonstrate the superior performance of SRF-ML compared to traditional machine learning approaches, highlighting its potential to revolutionize partner selection processes in the realm of international trade. The results demonstrate the superior performance of SRF-ML compared to traditional machine learning approaches, with accuracy improvements ranging from 5% to 10%. By leveraging advanced feature sets and model configurations, SRF-ML enables decision-makers to make more informed and strategic decisions, ultimately enhancing the efficiency and effectiveness of global trade operations.

Cross-View Geo-Localization via Learning Disentangled Geometric Layout Correspondence

Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task, its difficulties root in the drastic view changes and different capturing time between two views. Despite these difficulties, recent works achieve outstanding progress on cross-view geo-localization benchmarks. However, existing methods still suffer from poor performance on the cross-area benchmarks, in which the training and testing data are captured from two different regions. We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set. In this paper, we propose GeoDTR which explicitly disentangles geometric information from raw features and learns the spatial correlations among visual features from aerial and ground pairs with a novel geometric layout extractor module. This module generates a set of geometric layout descriptors, modulating the raw features and producing high-quality latent representations. In addition, we elaborate on two categories of data augmentations, (i) Layout simulation, which varies the spatial configuration while keeping the low-level details intact. (ii) Semantic augmentation, which alters the low-level details and encourages the model to capture spatial configurations. These augmentations help to improve the performance of the cross-view geo-localization models, especially on the cross-area benchmarks. Moreover, we propose a counterfactual-based learning process to benefit the geometric layout extractor in exploring spatial information. Extensive experiments show that GeoDTR not only achieves state-of-the-art results but also significantly boosts the performance on same-area and cross-area benchmarks. Our code can be found at https://gitlab.com/vail-uvm/geodtr.

Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration

Feature models (FMs) appeared more than 30 years ago, and they are valuable tools for modeling the functional variability of systems. The automated analysis of feature models (AAFM) is currently a thriving, motivating, and active research area. The product configuration of FMs is a relevant and helpful operation, a crucial activity overall with large-scale feature models. The minimal conflict detection, the diagnosis of in-conflict configuration, and the product completion of consistent partial configuration are significant operations for obtaining consistent and well-defined products. Overall, configuring products for large-scale variability intensive systems (VIS) asks for efficient automated solutions for minimal conflict, diagnosis, and product configuration. Given the relevance of minimal conflict, diagnosis, and product configuration, and the current application of large-scale configuration and FMs for representing those systems and products, the main goals of this research paper are to establish the fundaments of the product configuration of feature models and systematically review existing solutions for the conflict detection, diagnosis, and product completion in FMs from 2010 to 2019. We can perceive that even though modern computing approaches exist for AAFM operations, no solutions exist for assisting the product configurations before 2020. This article reports that in 2020, new solutions appear regarding applying parallel computing for those goals. This research highlights research opportunities for developing new and more efficient solutions for conflict detection, diagnosis, and product completion of large-scale configurations.

Predicting the citation count and CiteScore of journals one year in advance

Prediction of the future performance of academic journals is a task that can benefit a variety of stakeholders including editorial staff, publishers, indexing services, researchers, university administrators and granting agencies. Using historical data on journal performance, this can be framed as a machine learning regression problem. In this work, we study two such regression tasks: 1) prediction of the number of citations a journal will receive during the next calendar year, and 2) prediction of the Elsevier CiteScore a journal will be assigned for the next calendar year. To address these tasks, we first create a dataset of historical bibliometric data for journals indexed in Scopus. We propose the use of neural network models trained on our dataset to predict the future performance of journals. To this end, we perform feature selection and model configuration for a Multi-Layer Perceptron and a Long Short-Term Memory. Through experimental comparisons to heuristic prediction baselines and classical machine learning models, we demonstrate superior performance in our proposed models for the prediction of future citation and CiteScore values.

Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

Most large-scale systems including self-adaptive systems utilize feature models (FMs) to represent their complex architectures and benefit from the reuse of commonalities and variability information. Self-adaptive systems (SASs) are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts. However, reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources. The process of configuration reuse can be a better alternative to some contexts to reduce computational time, effort and error-prone. Nevertheless, systems’ complexity can be reduced while the development process of systems by reusing elements or components. FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components. While current FM-based modelling techniques represent, manage, and reuse elementary features to model SASs concepts, modeling and reusing configurations have not yet been considered. In this context, this study presents an extension to FMs by introducing and managing configuration features and their reuse process. Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

Dynamic Software Product Lines (DSPLs) are a well-accepted approach to self-adaptation at runtime. In the context of DSPLs, there are plenty of reactive approaches that apply countermeasures as soon as a context change happens. In this paper we propose a proactive approach, ProDSPL, that exploits an automatically learnt model of the system, anticipates future variations of the system and generates the best DSPL configuration that can lessen the negative impact of future events on the quality requirements of the system. Predicting the future fosters adaptations that are good for a longer time and therefore reduces the number of reconfigurations required, making the system more stable.ProDSPL formulates the problem of the generation of dynamic reconfigurations as a proactive controller over a prediction horizon, which includes a mapping of the valid configurations of the DSPL into linear constraints. Our approach is evaluated and compared with a reactive approach, DAGAME, also based on a DSPL, which uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. ProDSPL has been evaluated using a strategy mobile game and a set of randomly generated feature models. The evaluation shows that ProDSPL gives good results with regard to the quality of the configurations generated when it tries anticipate future events. Moreover, in doing so, ProDSPL enforces the system to make as few reconfigurations as possible.

Automated composition and optimization of services for variability-intensive domains

The growth in the number of publicly available services on the Web has encouraged developers to rely more heavily on such services to deliver products in a faster, cheaper and more reliable fashion. Many developers are now using a collection of these services in tandem to build their applications. While there has been much attention to the area of service composition, there are few works that examine the possibility of automatically generating service compositions for variability-intensive application domains. High variability in a domain is often captured through an organized feature space, which has the potential for developing many different application instantiations. The focus of our work is to develop an end-to-end technique that would enable the automatic generation of composite services based on a specific configuration of the feature space that would be directly executable and presented in WS-BPEL format. To this end, we adopt concepts from software product line engineering and AI planning to deliver the automated composition of online services. We will further benefit from such notions as safeness and threat from AI planning to optimize the generated service compositions by introducing parallelism where possible. Furthermore, we show how the specification of the generated service composition can be translated into executable WS-BPEL code. More specifically, the core contributions of our work are: (1) we show how AI planning techniques can be used to generate a workflow based on a feature model configuration; (2) we propose a method for optimizing a workflow generated based on AI planning techniques; and (3) we demonstrate that the optimized workflow can be directly translated into WS-BPEL code. We evaluate our work from two perspectives: (i) we will first formally prove that the methods that we have proposed are sound and complete from a theoretical perspective, and (ii) we will show through experimentation that our proposed work is usable from a practical point of view.

Personalized recommender systems for product-line configuration processes

Product lines are designed to support the reuse of features across multiple products. Features are product functional requirements that are important to stakeholders. In this context, feature models are used to establish a reuse platform and allow the configuration of multiple products through the interactive selection of a valid combination of features. Although there are many specialized configurator tools that aim to provide configuration support, they only assure that all dependencies from selected features are automatically satisfied. However, no support is provided to help decision makers focus on likely relevant configuration options. Consequently, since decision makers are often unsure about their needs, the configuration of large feature models becomes challenging. To improve the efficiency and quality of the product configuration process, we propose a new approach that provides users with a limited set of permitted, necessary and relevant choices. To this end, we adapt six state-of-the-art recommender algorithms to the product line configuration context. We empirically demonstrate the usability of the implemented algorithms in different domain scenarios, based on two real-world datasets of configurations. The results of our evaluation show that recommender algorithms, such as CF-shrinkage, CF-significance weighting, and BRISMF, when applied in the context of product-line configuration can efficiently support decision makers in a most efficient selection of features.

Toward automated quality-centric product line configuration using intentional variability

Software product line engineering is a discipline that facilitates a systematic reuse-based approach by formally representing commonalities and variabilities between the applications of a target domain. As one of the main artifacts of the software product line, a feature model represents the possible configuration space and can be customized based on the stakeholders' needs. Considering the complexity of the variabilities represented by feature models and the diversity of the stakeholders' expectations, the configuration process can be viewed as a complex optimization problem. In previous research, researchers have bridged the gap between requirement and product line engineering by integrating feature models and goal models. In this paper, we propose an approach for the configuration process that seeks to satisfy the stakeholders' requirements as well as the feature models' structural and integrity constraints. We model stakeholders' functional and nonfunctional needs and their preferences using requirement engineering goal models. We formalize the structure of the feature model, the stakeholders' objectives, and their preferences in the form of an integer linear program to conduct a semi-automated feature model configuration process. Our experimental results show that the proposed configuration framework is scalable when considering both functional and nonfunctional requirements of stakeholders.

Degenerate motions in multicamera cluster SLAM with non-overlapping fields of view

An analysis of the relative motion and point feature model configurations leading to solution degeneracy is presented, for the case of a Simultaneous Localization and Mapping system using multicamera clusters with non-overlapping fields-of-view. The SLAM optimization system seeks to minimize image space reprojection error and is formulated for a cluster containing any number of component cameras, observing any number of point features over two keyframes. The measurement Jacobian is transformed to expose a reduced-dimension representation such that the degeneracy of the system can be determined by the rank of a dense submatrix. A set of relative motions sufficient for degeneracy are identified for certain cluster configurations, independent of target model geometry. Furthermore, it is shown that increasing the number of cameras within the cluster and observing features across different cameras over the two keyframes reduces the size of the degenerate motion sets significantly.

Features as transformations: A generative approach to software development

The objective of feature modeling is to foster software reuse by enabling to explicitly and abstractly express commonality and variability in the domain. Feature modeling is used to configure other models and, eventually, code. These software assets are being configured by the feature model based on the selection of variable features. However, selecting a feature is far from a naive component based approach where feature inclusion would simply mean including the corresponding component. More often than not, feature inclusion affects several places in models or code to be configured requiring their nontrivial adaptation. Thus, feature inclusion recalls transformation and this is at heart of the approach to feature model driven generation of software artifacts proposed in this paper. Features are viewed as transformations that may be executed during the generative process conducted by the feature model configuration. The generative process is distributed in respective transformations enabling the developers to have a better control over it. This approach can be applied to modularize changes in product customization and to establish generative software product lines by gradual refactoring of existing products.

The effects of visualization and interaction techniques on feature model configuration

A Software Product Line is a set of software systems of a domain, which share some common features but also have significant variability. A feature model is a variability modeling artifact which represents differences among software products with respect to variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by selecting features in the feature model (referred to as the configuration process) and by instantiating the reference model. However, feature model configuration can be a cumbersome task because: 1) feature models may consist of a large number of features, which are hard to comprehend and maintain; and 2) many factors including technical limitations, implementation costs, stakeholders' requirements and expectations must be considered in the configuration process. Recognizing these issues, a significant amount of research efforts has been dedicated to different aspects of feature model configuration such as automating the configuration process. Several approaches have been proposed to alleviate the feature model configuration challenges through applying visualization and interaction techniques. However, there have been limited empirical insights available into the impact of visualization and interaction techniques on the feature model configuration process. In this paper, we present a set of visualization and interaction interventions for representing and configuring feature models, which are then empirically validated to measure the impact of the proposed interventions. An empirical study was conducted by following the principles of control experiments in software engineering and by applying the well-known software quality standard ISO 9126 to operationalize the variables investigated in the experiment. The results of the empirical study revealed that the employed visualization and interaction interventions significantly improved completion time of comprehension and changing of the feature model configuration. Additionally, according to results, the proposed interventions are easy-to-use and easy-to-learn for the participants.

A Generic Process for the Design and Generation of Software Product Line Skeleton Architectures

This paper presents a process, a method and a framework for developing families of software systems in a domain. The process is generic (domain-independent) and produces skeleton software architectures as Software Product Lines. The genericity is supported by the metamodels (abstract languages) that are defined in order to describe the Reference Architecture (structure view, behavior view and variability view) of the system domain. A standardized Production Plan takes the Reference Architecture as input and produces the equivalent Skeleton Software Architecture (component-connector view) using a Feature Model configuration (describing the system to be) as output. This Skeleton Software Architecture includes the structure and behavior of the target software product. A framework has been implemented to support the approach. The process is applied, as an example, to the Diagnostic Expert Systems domain. Our approach is based on Model-Driven Engineering techniques and the Software Product Line paradigm. A domain analysis must be done in order to build the Reference Architecture.

Toward automated feature model configuration with optimizing non-functional requirements

ContextA software product line is a family of software systems that share some common features but also have significant variabilities. A feature model is a variability modeling artifact, which represents differences among software products with respect to the variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by binding the variation points in the feature model (called configuration process) and by instantiating the reference model. ObjectiveIn this work we address the feature model configuration problem and propose a framework to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. Additionally, interdependencies between various non-functional properties are taken into account in the framework. MethodThe proposed framework combines Analytical Hierarchy Process (AHP) and Fuzzy Cognitive Maps (FCM) to compute the non-functional properties weights based on stakeholders’ preferences and interdependencies between non-functional properties. Afterwards, Hierarchical Task Network (HTN) planning is applied to find the optimal feature model configuration. ResultOur approach improves state-of-art of feature model configuration by considering positive or negative impacts of the features on non-functional properties, the stakeholders’ preferences, and non-functional interdependencies. The approach presented in this paper extends earlier work presented in [1] from several distinct perspectives including mechanisms handling interdependencies between non-functional properties, proposing a novel tooling architecture, and offering visualization and interaction techniques for representing functional and non-functional aspects of feature models. Conclusionour experiments show the scalability of our configuration approach when considering both functional and non-functional requirements of stakeholders.

An Automatic-Propagation Strategy and Selective-Undo Mechanism for Feature Model Configuration

An Automatic-Propagation Strategy and Selective-Undo Mechanism for Feature Model Configuration

Evolving feature model configurations in software product lines

The increasing complexity and cost of software-intensive systems has led developers to seek ways of reusing software components across development projects. One approach to increasing software reusability is to develop a software product-line (SPL), which is a software architecture that can be reconfigured and reused across projects. Rather than developing software from scratch for a new project, a new configuration of the SPL is produced. It is hard, however, to find a configuration of an SPL that meets an arbitrary requirement set and does not violate any configuration constraints in the SPL.Existing research has focused on techniques that produce a configuration of an SPL in a single step. Budgetary constraints or other restrictions, however, may require multi-step configuration processes. For example, an aircraft manufacturer may want to produce a series of configurations of a plane over a span of years without exceeding a yearly budget to add features.This paper provides three contributions to the study of multi-step configuration for SPLs. First, we present a formal model of multi-step SPL configuration and map this model to constraint satisfaction problems (CSPs). Second, we show how solutions to these SPL configuration problems can be automatically derived with a constraint solver by mapping them to CSPs. Moreover, we show how feature model changes can be mapped to our approach in a multi-step scenario by using feature model drift. Third, we present empirical results demonstrating that our CSP-based reasoning technique can scale to SPL models with hundreds of features and multiple configuration steps.

Dynamic decision models for staged software product line configuration

Software product line engineering practices offer desirable characteristics such as rapid product development, reduced time-to-market, and more affordable development costs as a result of systematic representation of the variabilities of a domain of discourse that leads to methodical reuse of software assets. The development lifecycle of a product line consists of two main phases: domain engineering, which deals with the understanding and formally modeling of the target domain, and application engineering that is concerned with the configuration of a product line into one concrete product based on the preferences and requirements of the stakeholders. The work presented in this paper focuses on the application engineering phase and builds both the theoretical and technological tools to assist the stakeholders in (a) understanding the complex interactions of the features of a product line; (b) eliciting the utility of each feature for the stakeholders and hence exposing the stakeholders’ otherwise implicit preferences in a way that they can more easily make decisions; and (c) dynamically building a decision model through interaction with the stakeholders and by considering the structural characteristics of software product line feature models, which will guide the stakeholders through the product configuration process. Initial exploratory empirical experiments that we have performed show that our proposed approach for helping stakeholders understand their feature preferences and its associated staged feature model configuration process is able to positively impact the quality of the end results of the application engineering process within the context of the limited number of participants. In addition, it has been observed that the offered tooling support is able to ease the staged feature model configuration process.

Visualization of variability and configuration options

When designing, constructing, and maintaining diverse and variable software systems, a key challenge is the complexity of systems. A potential approach to tackle this challenge are techniques from variability management and product line engineering to handle the diversity and variability. A key asset in variability management is a variability model, which explicitly specifies the commonalities and variability of a system and the constraints between variants. However, handling variability and configurations remains a challenge due to the complexity on a cognitive level as human engineers reach their limits in identifying, understanding, and using all relevant details. In this paper we address this issue by providing concepts for interactive visual tool support for the configuration of systems with the help of feature models. We discuss relevant principles from the area of information visualization and their application to the domain of feature model configuration. We discuss techniques for interactive configuration support based on a reasoning engine, which, e.g., ensures the validity of configurations. We illustrate our findings by a concrete tool solution called S2T2 Configurator.

Architectural evolution of FamiWare using cardinality-based feature models

ContextAmbient Intelligence systems domain is an outstanding example of modern systems that are in permanent evolution, as new devices, technologies or facilities are continuously appearing. This means it would be desirable to have a mechanism that helps with the propagation of evolution changes in deployed systems. ObjectiveWe present a software product line engineering process to manage the evolution of FamiWare, a family of middleware for ambient intelligence environments. This process drives the evolution of FamiWare middleware configurations using cardinality-based feature models, which are especially well suited to express the structural variability of ambient intelligence systems. MethodFamiWare uses cardinality-based feature models and clonable features to model the structural variability present in ambient intelligence systems, composed of a large variety of heterogeneous devices. Since the management evolution of configurations with clonable features is manually untreatable due to the high number of features, our process automates it and propagates changes made at feature level to the architectural components of the FamiWare middleware. This is a model driven development process as the evolution management, the propagation of evolution changes and the code generation are performed using some kind of model mappings and transformations. Concretely we present a variability modelling language to map the selection of features to the corresponding FamiWare middleware architectural components. ResultsOur process is able to manage the evolution of cardinality-based feature models with thousands of features, something which is not possible to tackle manually. Thanks to the use of the variability language and the automatic code generation it is possible to propagate and maintain a correspondence between the FamiWare architectural model and the code. The process is then able to calculate the architectural differences between the evolved configuration and the previous one. Checking these differences, our process helps to calculate the effort needed to perform the evolution changes in the customized products. To perform those tasks we have defined two operators, one to calculate the differences between two feature model configurations and another to create a new configuration from a previous one. ConclusionOur process automatically propagates the evolution changes of the middleware family into the existing configurations where the middleware is already deployed and also helps us to calculate the effort in performing the changes in every configuration. Finally, we validated our approach, demonstrating the functioning of the defined operators and showing that by using our tool we can generate evolved configurations for FamiWare with thousands of cloned features, for several case studies.

Formalizing interactive staged feature model configuration

SUMMARYFeature modeling an attractive technique for capturing commonality as well as variability within an application domain for generative programming and software product line engineering. Feature models symbolize an overarching representation of the possible application configuration space, and can hence be customized based on specific domain requirements and stakeholder goals. Most interactive or semi‐automated feature model customization processes neglect the need to have a holistic approach towards the integration and satisfaction of the stakeholder's soft and hard constraints, and the application‐domain integrity constraints. In this paper, we will show how the structure and constraints of a feature model can be modeled uniformly through Propositional Logic extended with concrete domains, called Pscr(𝒩). Furthermore, we formalize the representation of soft constraints in fuzzy 𝒫(𝒩) and explain how semi‐automated feature model customization is performed in this setting. The model configuration derivation process that we propose respects the soundness and completeness properties. Copyright © 2011 John Wiley & Sons, Ltd.