Feature Model Configuration Based on Two-Layer Modelling in Software Product Lines

The aim of the Software Product Line (SPL) approach is to improve the software development process by producing software products that match the stakeholders’ requirements. One of the important topics in SPLs is the feature model (FM) configuration process. The purpose of configuration here is to select and remove specific features from the FM in order to produce the required software product. At the same time, detection of differences between application’s requirements and the available capabilities of the implementation platform is a major concern of application requirements engineering. It is possible that the implementation of the selected features of FM needs certain software and hardware infrastructures such as database, operating system and hardware that cannot be made available by stakeholders. We address the FM configuration problem by proposing a method, which employs a two-layer FM comprising the application and infrastructure layers. We also show this method in the context of a case study in the SPL of a sample E-Shop website. The results demonstrate that this method can support both functional and non-functional requirements and can solve the problems arising from lack of attention to implementation requirements in SPL FM selection phase

Interactive Preference-based Approach to Optimal Feature Configuration of Multi-dimensional Service Platforms

A multi-dimensional service delivery platform (MDSP) supports development, deployment and management of services in multiple business domains, serves multiple consumers with different functional and non-functional requirements and integrates services from diverse external collaborators to actualize the platform’s business objective. Consumers of product line services have variant needs that are based on the specific requirements of their business objectives, which demands optimal configuration of the MDSP. Optimal configuration of the MDSP connotes the existence of the most appropriate set of features on the MDSP that best approximates the consumer’s requirements, in the face of multiple conflicting objectives. So far, solutions proposed in the literature have mainly used either a priori or a-posterior methods. In prior methods, the requirements and preference information is provided before the configuration process begins; while a set of possible configurations is first generated and preferred selection is made from the set in a-posterior methods. These methods lack the kind of flexibility afforded by interactive methods in an attempt to generate satisfactory results. The aim of this research is to develop an approach that engenders the derivation of optimal configurations from a multi-dimensional service platform (MDSP), in a manner that is interactive and meets the needs of the consumer

User-centric Adaptation Analysis of Multi-tenant Services

Multi-tenancy is a key pillar of cloud services. It allows different users to share computing and virtual resources transparently, meanwhile guaranteeing substantial cost savings. Due to the tradeoff between scalability and customization, one of the major drawbacks of multi-tenancy is limited configurability. Since users may often have conflicting configuration preferences, offering the best user experience is an open challenge for service providers. In addition, the users, their preferences, and the operational environment may change during the service operation, thus jeopardizing the satisfaction of user preferences. In this article, we present an approach to support user-centric adaptation of multi-tenant services. We describe how to engineer the activities of the Monitoring, Analysis, Planning, Execution (MAPE) loop to support user-centric adaptation, and we focus on adaptation analysis. Our analysis computes a service configuration that optimizes user satisfaction, complies with infrastructural constraints, and minimizes reconfiguration obtrusiveness when user- or service-related changes take place. To support our analysis, we model multitenant services and user preferences by using feature and preference models, respectively. We illustrate our approach by utilizing different cases of virtual desktops. Our results demonstrate the effectiveness of the analysis in improving user preferences satisfaction in negligible time.Ministerio de Economía y Competitividad TIN2012-32273Junta de Andalucía P12--TIC--1867Junta de Andalucía TIC-590

An Empirical Evaluation of Constrained Feature Selection

While feature selection helps to get smaller and more understandable prediction models, most existing feature-selection techniques do not consider domain knowledge. One way to use domain knowledge is via constraints on sets of selected features. However, the impact of constraints, e.g., on the predictive quality of selected features, is currently unclear. This article is an empirical study that evaluates the impact of propositional and arithmetic constraints on filter feature selection. First, we systematically generate constraints from various types, using datasets from different domains. As expected, constraints tend to decrease the predictive quality of feature sets, but this effect is non-linear. So we observe feature sets both adhering to constraints and with high predictive quality. Second, we study a concrete setting in materials science. This part of our study sheds light on how one can analyze scientific hypotheses with the help of constraints

ETHOM: An Evolutionary Algorithm for Optimized Feature Models Generation (v. 1.2): Technical Report ISA-2012-TR-05

A feature model defines the valid combinations of features in a domain. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. The progress of this discipline is leading to an increasing concern to test and compare the performance of analysis solutions using tough input models that show the behaviour of the tools in extreme situations (e.g. those producing longest execution times or highest memory consumption). Currently, these feature models are generated randomly ignoring the internal aspects of the tools under tests. As a result, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this technical report, we model the problem of finding computationally– hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm. Given a tool and an analysis operation, our algorithm generates input models of a predefined size maximizing aspects as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the behaviour of tools in pessimistic cases providing a better idea of their real power. Experiments using our evolutionary algorithm on a number of analysis operations and tools have successfully identified input models causing much longer executions times and higher memory consumption than random models of identical or even larger size. Our solution is generic and applicable to a variety of optimization problems on feature models, not only those involving analysis operations. In view of the positive results, we expect this work to be the seed for a new wave of research contributions exploiting the benefit of evolutionary programming in the field of feature modelling

Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints