Ebrahim Bagheri

Assessing the maintainability of software product line feature models using structural metrics

A software product line is a unified representation of a set of conceptually similar software systems that share many common features and satisfy the requirements of a particular domain. Within the context of software product lines, feature models are tree-like structures that are widely used for modeling and representing the inherent commonality and variability of software product lines. Given the fact that many different software systems can be spawned from a single software product line, it can be anticipated that a low-quality design can ripple through to many spawned software systems. Therefore, the need for early indicators of external quality attributes is recognized in order to avoid the implications of defective and low-quality design during the late stages of production. In this paper, we propose a set of structural metrics for software product line feature models and theoretically validate them using valid measurement-theoretic principles. Further, we investigate through controlled experimentation whether these structural metrics can be good predictors (early indicators) of the three main subcharacteristics of maintainability: analyzability, changeability, and understandability. More specifically, a four-step analysis is conducted: (1) investigating whether feature model structural metrics are correlated with feature model maintainability through the employment of classical statistical correlation techniques; (2) understanding how well each of the structural metrics can serve as discriminatory references for maintainability; (3) identifying the sufficient set of structural metrics for evaluating each of the subcharacteristics of maintainability; and (4) evaluating how well different prediction models based on the proposed structural metrics can perform in indicating the maintainability of a feature model. Results obtained from the controlled experiment support the idea that useful prediction models can be built for the purpose of evaluating feature model maintainability using early structural metrics. Some of the structural metrics show significant correlation with the subjective perception of the subjects about the maintainability of the feature models.

The state of the art in critical infrastructure protection: a framework for convergence

The protection of critical infrastructure systems has recently become a major concern for many countries. This is due to the effect of these systems on the daily lives of all citizens and the high possibility of disruption because of their complex structure and hidden interdependencies, which subsequently attract the attention of many researchers and scientists. The investigations of researchers have encompassed issues of national security, policymaking, infrastructure system organisation, and behaviour analysis and modelling. In this paper, we look into the latter subject and explore the attempts that have been made. Based on the available schemes and the requirements of this area, we propose a five-dimensional framework that introduces the major research necessities in this field. Among the various available schemes, we study ten of the most recently developed and/or influential systems. A comparison of these schemes based on the features of our proposed framework is made. The comparison allows us to conclude our examination with the identification of current research strengths and guidelines for future work.

Evolutionary search-based test generation for software product line feature models

Product line-based software engineering is a paradigm that models the commonalities and variabilities of different applications of a given domain of interest within a unique framework and enhances rapid and low cost development of new applications based on reuse engineering principles. Despite the numerous advantages of software product lines, it is quite challenging to comprehensively test them. This is due to the fact that a product line can potentially represent many different applications; therefore, testing a single product line requires the test of its various applications. Theoretically, a product line with n software features can be a source for the development of 2n application. This requires the test of 2n applications if a brute-force comprehensive testing strategy is adopted. In this paper, we propose an evolutionary testing approach based on Genetic Algorithms to explore the configuration space of a software product line feature model in order to automatically generate test suites. We will show through the use of several publicly-available product line feature models that the proposed approach is able to generate test suites of O(n) size complexity as opposed to O(2n) while at the same time form a suitable tradeoff balance between error coverage and feature coverage in its generated test suites.

Configuring software product line feature models based on Stakeholders' soft and hard requirements

Feature modeling is a technique for capturing commonality and variability. Feature models symbolize a representation of the possible application configuration space, and can be customized based on specific domain requirements and stakeholder goals. Most feature model configuration processes neglect the need to have a holistic approach towards the integration and satisfaction of the stakeholders 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 P(N). Furthermore, we formalize the representation of soft constraints in fuzzy P(N) and explain how semi-automated feature model configuration is performed. The model configuration derivation process that we propose respects the soundness and completeness properties.

Formalizing interactive staged feature model configuration

Feature 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 stakeholders 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

UML-CI: A reference model for profiling critical infrastructure systems

The study of critical infrastructure systems organization and behavior has drawn great attention in the recent years. This is in part due to their great influence on the ordinary life of every citizen. In this paper, we study critical infrastructures’ characteristics and propose a reference model based on the Unified Modeling Language (UML). This reference model attempts to provide suitable means for the task of modeling an infrastructure system through offering five major metamodels. We introduce each of these metamodels and explain how it is possible to integrate them into a unique representation to characterize various aspects of an infrastructure system. Based on the metamodels of UML-CI, infrastructure system knowledge bases can be built to aid the process of infrastructure system modeling, profiling, and management.

A metrics-driven approach for quality assessment of linked open data

The main objective of the Web of Data paradigm is to crystallize knowledge through the interlinking of already existing but dispersed data. The usefulness of the developed knowledge depends strongly on the quality of the published data. Researchers have observed many deficiencies with regard to the quality of Linked Open Data. The first step towards improving the quality of data released as a part of the Linked Open Data Cloud is to develop tools for measuring the quality of such data. To this end, the main objective of this paper is to propose and validate a set of metrics for evaluating the inherent quality characteristics of a dataset before it is released to the Linked Open Data Cloud. These inherent characteristics are semantic accuracy, syntactic accuracy, uniqueness, completeness and consistency. We follow the Goal-Question-Metric approach to propose various metrics for each of these five quality characteristics. We provide both theoretical validation and empirical observation of the behavior of the proposed metrics in this paper. The proposed set of metrics establishes a starting point for a systematic inherent quality analysis of open datasets.

A quality aggregation model for service-oriented software product lines based on variability and composition patterns

Quality evaluation is a challenging task in monolithic software systems. It is even more complex when it comes to Service-Oriented Software Product Lines (SOSPL), as it needs to analyze the attributes of a family of SOA systems. In SOSPL, variability can be planned and managed at the architectural level to develop a software product with the same set of functionalities but different degrees of non-functional quality attribute satisfaction. Therefore, architectural quality evaluation becomes crucial due to the fact that it allows for the examination of whether or not the final product satisfies and guarantees all the ranges of quality requirements within the envisioned scope. This paper addresses the open research problem of aggregating QoS attribute ranges with respect to architectural variability. Previous solutions for quality aggregation do not consider architectural variability for composite services. Our approach introduces variability patterns that can possibly occur at the architectural level of an SOSPL. We propose an aggregation model for QoS computation which takes both variability and composition patterns into account.

Goal-Oriented Test Case Selection and Prioritization for Product Line Feature Models

The software product line engineering paradigm is amongst the widely used means for capturing and handling the commonalities and variabilities of the many applications of a target domain. The large number of possible products and complex interactions between software product line features makes the effective testing of them a challenge. To conquer the time and space complexity involved with testing a product line, an intuitive approach is the reduction of the test space. In this paper, we propose an approach to reduce the product line test space. We introduce a goal-oriented approach for the selection of the most desirable features from the product line. Such an approach allows us to identify the features that are more important and need to be tested more comprehensively from the perspective of the domain stakeholders. The more important features and the configurations that contain them will be given priority over the less important configurations, hence providing a hybrid test case reduction and prioritization strategy for testing software product lines.

AUTOMATED STAGED CONFIGURATION WITH SEMANTIC WEB TECHNOLOGIES

Since the introduction in the early nineties, feature models receive a great deal of attention in industry and academia. Industrial success stories in applying feature models for modeling software product lines, and using them for configuring software-intensive systems motivate academia to discover ways to integrate different feature dependencies into the feature model, and automate verified feature configuration. In this paper we demonstrate how ontologies and Semantic Web technologies facilitate seamless integration of required external services and deployment platform capabilities into the feature model. Furthermore, we also contribute with an algorithm for automating staged configuration using Semantic Web reasoners to discover unfeasible features of the feature model.