Breathing ontological knowledge into feature model synthesis: an empirical study

Feature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. As the manual construction of an FM is time-consuming and error-prone, management operations have been developed for reverse engineering, merging, slicing, or refactoring FMs from a set of configurations/dependencies. Yet the synthesis of meaningless ontological relations in the FM – as defined by its feature hierarchy and feature groups – may arise and cause severe difficulties when reading, maintaining or exploiting it. Numerous synthesis techniques and tools have been proposed, but only a few consider both configuration and ontological semantics of an FM. There are also few empirical studies investigating ontological aspects when synthesizing FMs. In this article, we define a generic, ontologic-aware synthesis procedure that computes the likely siblings or parent candidates for a given feature. We develop six heuristics for clustering and weighting the logical, syntactical and semantical relationships between feature names. We then perform an empirical evaluation on hundreds of FMs, coming from the SPLOT repository and Wikipedia. We provide evidence that a fully automated synthesis (i.e., without any user intervention) is likely to produce FMs far from the ground truths. As the role of the user is crucial, we empirically analyze the strengths and weaknesses of heuristics for computing ranking lists and different kinds of clusters. We show that a hybrid approach mixing logical and ontological techniques outperforms state-of-the-art solutions. We believe our approach, environment, and empirical results support researchers and practitioners working on reverse engineering and management of FMs.     

Improving quality of software product line by analysing inconsistencies in feature models using an ontological rule‐based approach

In software product line engineering, feature models (FMs) represent the variability and commonality of a family of software products. The development of FMs may introduce inaccurate feature relationships. These relationships may cause various types of defects such as inconsistencies, which deteriorate the quality of software products. Several researchers have worked on the identification of defects due to inconsistency in FMs, but only a few of them have explained their causes. In this paper, FM is transformed to predicate‐based feature model ontology using Prolog. Further, first‐order logic is employed for defining rules to identify defects due to inconsistency, the explanations for their causes, and suggestions for their corrections. The proposed approach is explained using an FM available in Software Product Line Online Tools repository. It is validated using 26 FMs of discrete sizes up to 5,543 features, generated using the FeatureIDE tool and real‐world FMs. Results indicate that the proposed methodology is effective, accurate, and scalable and improves software product line.     

Automated metamorphic testing on the analyses of feature models

Context

A feature model (FM) represents the valid combinations of features in a domain. The automated extraction of information from FMs is a complex task that involves numerous analysis operations, techniques and tools. Current testing methods in this context are manual and rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses, this is known as the oracle problem.

Objective

In this paper, we propose using metamorphic testing to automate the generation of test data for feature model analysis tools overcoming the oracle problem. An automated test data generator is presented and evaluated to show the feasibility of our approach.

Method

We present a set of relations (so-called metamorphic relations) between input FMs and the set of products they represent. Based on these relations and given a FM and its known set of products, a set of neighbouring FMs together with their corresponding set of products are automatically generated and used for testing multiple analyses. Complex FMs representing millions of products can be efficiently created by applying this process iteratively.

Results

Our evaluation results using mutation testing and real faults reveal that most faults can be automatically detected within a few seconds. Two defects were found in FaMa and another two in SPLOT, two real tools for the automated analysis of feature models. Also, we show how our generator outperforms a related manual suite for the automated analysis of feature models and how this suite can be used to guide the automated generation of test cases obtaining important gains in efficiency.

Conclusion

Our results show that the application of metamorphic testing in the domain of automated analysis of feature models is efficient and effective in detecting most faults in a few seconds without the need for a human oracle.     

Deriving products for variability test of Feature Models with a hyper-heuristic approach

Deriving products from a Feature Model (FM) for testing Software Product Lines (SPLs) is a hard task. It is important to select a minimum number of products but, at the same time, to consider the coverage of testing criteria such as pairwise, among other factors. To solve such problems Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied. However, to design a solution for this and other software engineering problems can be very difficult, because it is necessary to choose among different search operators and parameters. Hyper-heuristics can help in this task, and have raised interest in the Search-Based Software Engineering (SBSE) field. Considering the growing adoption of SPL in the industry and crescent demand for SPL testing approaches, this paper introduces a hyper-heuristic approach to automatically derive products to variability testing of SPLs. The approach works with MOEAs and two selection methods, random and based on FRR-MAB (Fitness Rate Rank based Multi-Armed Bandit). It was evaluated with real FMs and the results show that the proposed approach outperforms the traditional algorithms used in the literature, and that both selection methods present similar performance.     

Intelligent software product line configurations: A literature review

A software product line (SPL) is a set of industrial software-intensive systems for configuring similar software products in which personalized feature sets are configured by different business teams. The integration of these feature sets can generate inconsistencies that are typically resolved through manual deliberation. This is a time-consuming process and leads to a potential loss of business resources. Artificial intelligence (AI) techniques can provide the best solution to address this issue autonomously through more efficient configurations, lesser inconsistencies and optimized resources. This paper presents the first literature review of both research and industrial AI applications to SPL configuration issues. Our results reveal only 19 relevant research works which employ traditional AI techniques on small feature sets with no real-life testing or application in industry. We categorize these works in a typology by identifying 8 perspectives of SPL. We also show that only 2 standard industrial SPL tools employ AI in a limited way to resolve inconsistencies. To inject more interest and application in this domain, we motivate and present future research directions. Particularly, using real-world SPL data, we demonstrate how predictive analytics (a state of the art AI technique) can separately model inconsistent and consistent patterns, and then predict inconsistencies in advance to help SPL designers during the configuration of a product.     

On the consistency of knowledge bases: the COVADIS system

It is currently thought in the knowledge-based systems (KBS) domain that sophisticated tools are necessary for helping an expert with the difficult task of knowledge acquisition. The problem of detecting inconsistencies is especially crucial. The risk of inconsistencies increases with the size of the knowledge base; for large knowledge bases, detecting inconsistencies "by hand" or even by a superficial survey of the knowledge base is impossible. Indeed, most inconsistencies are due to the interaction between several rules via often deep deductions. In this paper, we first state the problem and define our approach in the framework of classical logic. We then describe a complete method to prove the consistency (or the inconsistency) of knowledge bases that we have implemented in the COVADIS system.     

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.     

An agent-oriented approach to change propagation in software maintenance

Software maintenance and evolution is a lengthy and expensive phase in the life cycle of a software system. In this paper we focus on the change propagation problem: given a primary change that is made in order to meet a new or changed requirement, what additional, secondary, changes are needed? We propose a novel, agent-oriented, approach that works by repairing violations of desired consistency rules in a design model. Such consistency constraints are specified using the Object Constraint Language (OCL) and the Unified Modelling Language (UML) metamodel, which form the key inputs to our change propagation framework. The underlying change propagation mechanism of our framework is based on the well-known Belief-Desire-Intention (BDI) agent architecture. Our approach represents change options for repairing inconsistencies using event-triggered plans, as is done in BDI agent platforms. This naturally reflects the cascading nature of change propagation, where each change (primary or secondary) can require further changes to be made. We also propose a new method for generating repair plans from OCL consistency constraints. Furthermore, a given inconsistency will typically have a number of repair plans that could be used to restore consistency, and we propose a mechanism for semi-automatically selecting between alternative repair plans. This mechanism, which is based on a notion of cost, takes into account cascades (where fixing the violation of a constraint breaks another constraint), and synergies between constraints (where fixing the violation of a constraint also fixes another violated constraint). Finally, we report on an evaluation of the approach, covering effectiveness, efficiency and scalability.     

An ontology change management approach for facility management

Facility management (FM) or technical property management is an approach to operate, maintain, improve and adapt buildings and infrastructures of organizations. A FM project requires the cooperation of many actors from different domains so it has to be automated in a constrained collaborative environment. This paper proposes a new approach for ontology change management applied on facility management of such projects. The industrial challenge is, firstly, to ensure consistency of a FM project knowledge from the construction phase to the technical property management phase (after delivery). Secondly, it has to provide to each actor of the project a personal up-to-date “view” of the building knowledge related to its business profile and allow its evolution. The scientific approach, called OntoVersionGraph, is a change management methodology for managing ontology life cycle including ontology evolution and versioning features, in conjunction with contextual view modeling. Its contribution is the impact management of changes between the ontology and its different views.     

Reasoning about product-line evolution using complex feature model differences

Features define common and variable parts of the members of a (software) product line. Feature models are used to specify the set of all valid feature combinations. Feature models not only enjoy an intuitive tree-like graphical syntax, but also a precise formal semantics, which can be denoted as propositional formulae over Boolean feature variables. A product line usually constitutes a long-term investment and, therefore, has to undergo continuous evolution to meet ever-changing requirements. First of all, product-line evolution leads to changes of the feature model due to its central role in the product-line paradigm. As a result, product-line engineers are often faced with the problems that (1) feature models are changed in an ad-hoc manner without proper documentation, and (2) the semantic impact of feature diagram changes is unclear. In this article, we propose a comprehensive approach to tackle both challenges. For (1), our approach compares the old and new version of the diagram representation of a feature model and specifies the changes using complex edit operations on feature diagrams. In this way, feature model changes are automatically detected and formally documented. For (2), we propose an approach for reasoning about the semantic impact of diagram changes. We present a set of edit operations on feature diagrams, where complex operations are primarily derived from evolution scenarios observed in a real-world case study, i.e., a product line from the automation engineering domain. We evaluated our approach to demonstrate its applicability with respect to the case study, as well as its scalability concerning experimental data sets.