Analysis Of Software Product Lines Research Articles

Learning by sampling: learning behavioral family models from software product lines

Family-based behavioral analysis operates on a single specification artifact, referred to as family model, annotated with feature constraints to express behavioral variability in terms of conditional states and transitions. Family-based behavioral modeling paves the way for efficient model-based analysis of software product lines. Family-based behavioral model learning incorporates feature model analysis and model learning principles to efficiently unify product models into a family model and integrate the behavior of various products into a behavioral family model. Albeit reasonably effective, the exhaustive analysis of product lines is often infeasible due to the potentially exponential number of valid configurations. In this paper, we first present a family-based behavioral model learning techniques, called FFSMDiff. Subsequently, we report on our experience on learning family models by employing product sampling. Using 105 products of six product lines expressed in terms of Mealy machines, we evaluate the precision of family models learned from products selected from different settings of the T-wise product sampling criterion. We show that product sampling can lead to models as precise as those learned by exhaustive analysis and hence, reduce the costs for family model learning.

Automated model-based performance analysis of software product lines under uncertainty

Context: A Software Product Line (SPL) can express the variability of a system through the specification of configuration options. Evaluating performance characteristics, such as the system response time and resource utilization, of a software product is challenging, even more so in the presence of uncertain values of the attributes. Objective: The goal of this paper is to automate the generation of performance models for software products derived from the feature model by selection heuristics. We aim at obtaining model-based predictive results to quantify the correlation between the features, along with their uncertainties, and the system performance. This way, software engineers can be informed on the performance characteristics before implementing the system. Method: We propose a tool-supported framework that, starting from a feature model annotated with performance-related characteristics, derives Queueing Network (QN) performance models for all the products of the SPL. Model-based performance analysis is carried out on the models obtained by selecting the products that show the maximum and minimum performance-based costs. Results: We applied our approach to almost seven thousand feature models including more than one hundred and seventy features. The generation of QN models is automatically performed in much less than one second, whereas their model-based performance analysis embeds simulation delays and requires about six minutes on average. Conclusion: The experimental results confirm that our approach can be effective on a variety of systems for which software engineers may be provided with early insights on the system performance in reasonably short times. Software engineers are supported in the task of understanding the performance bounds that may encounter when (de)selecting different configuration options, along with their uncertainties.

Feature-family-based reliability analysis of software product lines

ContextVerification techniques are being applied to ensure that software systems achieve desired quality levels and fulfill functional and non-functional requirements. However, applying these techniques to software product lines is challenging, given the exponential blowup of the number of products. Current product-line verification techniques leverage symbolic model checking and variability information to optimize the analysis, but still face limitations that make them costly or infeasible. In particular, state-of-the-art verification techniques for product-line reliability analysis are enumerative which hinders their applicability, given the latent exponential blowup of the configuration space. ObjectiveThe objectives of this paper are the following: (a) we present a method to efficiently compute the reliability of all configurations of a compositional or annotation-based software product line from its UML behavioral models, (b) we provide a tool that implements the proposed method, and (c) we report on an empirical study comparing the performance of different reliability analysis strategies for software product lines. MethodWe present a novel feature-family-based analysis strategy to compute the reliability of all products of a (compositional or annotation-based) software product line. The feature-based step of our strategy divides the behavioral models into smaller units that can be analyzed more efficiently. The family-based step performs the reliability computation for all configurations at once by evaluating reliability expressions in terms of a suitable variational data structure. ResultsOur empirical results show that our feature-family-based strategy for reliability analysis outperforms, in terms of time and space, four state-of-the-art strategies (product-based, family-based, feature-product-based, and family-product-based) for the same property. It is the only one that could be scaled to a 220-fold increase in the size of the configuration space. ConclusionOur feature-family-based strategy leverages both feature- and family-based strategies by taming the size of the models to be analyzed and by avoiding the products enumeration inherent to some state-of-the-art analysis methods.

Model-based safety analysis of software product lines

Software product lines (SPLs) provide an engineering basis for the systematic reuse of artefacts used for development, assessment, and management of critical embedded systems. Hazards and their causes are safety properties that may change according to the selection of variants in a particular SPL product. Therefore, safety analysis assets such as fault trees and failure modes and effects analysis (FMEA) cannot be directly reused because they are dependent upon the selection of product variants. In this paper, model-based safety analysis techniques and SPL variability management tools are used together to reduce the effort of product safety analysis by: reusing SPL hazard analysis, and providing automatic safety analysis for each SPL product. The benefit of applying the approach is the reduction of effort to perform product safety analysis. The proposed approach is illustrated using the Hephaestus variability management tool and the HiP-HOPS model-based safety analysis tool to generate fault trees, and FMEA for products of an automotive hybrid braking system SPL. The safety assessment artefacts generated by the approach provide feedback for the SPL development process helping safety engineers to make decisions earlier in the development lifecycle.

Model-based safety analysis of software product lines

Model-based safety analysis of software product lines

FLAME: a formal framework for the automated analysis of software product lines validated by automated specification testing

In a literature review on the last 20 years of automated analysis of feature models, the formalization of analysis operations was identified as the most relevant challenge in the field. This formalization could provide very valuable assets for tool developers such as a precise definition of the analysis operations and, what is more, a reference implementation, i.e., a trustworthy, not necessarily efficient implementation to compare different tools outputs. In this article, we present the FLAME framework as the result of facing this challenge. FLAME is a formal framework that can be used to formally specify not only feature models, but other variability modeling languages (VML s) as well. This reusability is achieved by its two-layered architecture. The abstract foundation layer is the bottom layer in which all VML-independent analysis operations and concepts are specified. On top of the foundation layer, a family of characteristic model layers—one for each VML to be formally specified—can be developed by redefining some abstract types and relations. The verification and validation of FLAME has followed a process in which formal verification has been performed traditionally by manual theorem proving, but validation has been performed by integrating our experience on metamorphic testing of variability analysis tools, something that has shown to be much more effective than manually designed test cases. To follow this automated, test-based validation approach, the specification of FLAME, written in Z, was translated into Prolog and 20,000 random tests were automatically generated and executed. Tests results helped to discover some inconsistencies not only in the formal specification, but also in the previous informal definitions of the analysis operations and in current analysis tools. After this process, the Prolog implementation of FLAME is being used as a reference implementation for some tool developers, some analysis operations have been formally specified for the first time with more generic semantics, and more VML s are being formally specified using FLAME.

Coherent branching feature bisimulation

Progress in the behavioral analysis of software product lines at the family level benefits from further development of the underlying semantical theory. Here, we propose a behavioral equivalence for feature transition systems (FTS) generalizing branching bisimulation for labeled transition systems (LTS). We prove that branching feature bisimulation for an FTS of a family of products coincides with branching bisimulation for the LTS projection of each the individual products. For a restricted notion of coherent branching feature bisimulation we furthermore present a minimization algorithm and show its correctness. Although the minimization problem for coherent branching feature bisimulation is shown to be intractable, application of the algorithm in the setting of a small case study results in a significant speed-up of model checking of behavioral properties.

AoURN-based modeling and analysis of software product lines

Software Product Line Engineering concerns itself with domain engineering and application engineering. During domain engineering, the whole product family is modeled with a preferred flavor of feature models and additional models as required (e.g., domain models or scenario-based models). During application engineering, the focus shifts toward a single family member and the configuration of the member's features. Recently, aspectual concepts have been employed to better encapsulate individual features of a Software Product Line (SPL), but the existing body of SPL work does not include a unified reasoning framework that integrates aspect-oriented feature description artifacts with the capability to reason about stakeholders' goals while taking feature interactions into consideration. Goal-oriented SPL approaches have been proposed, but do not provide analysis capabilities that help modelers meet the needs of the numerous stakeholders involved in an SPL while at the same time considering feature interactions. We present an aspect-oriented SPL approach for the requirements phase that allows modelers (a) to capture features, goals, and scenarios in a unified framework and (b) to reason about stakeholders' needs and perform trade-off analyses while considering undesirable interactions that are not obvious from the feature model. The approach is based on the Aspect-oriented User Requirements Notation (AoURN) and helps identify, prioritize, and choose products based on analysis results provided by AoURN editor and analysis tools. We apply the AoURN-based SPL framework to the Via Verde SPL to demonstrate the feasibility of this approach through the selection of a Via Verde product configuration that satisfies stakeholders' needs and results in a high-level, scenario-based specification that is free from undesirable feature interactions.

Supporting commonality-based analysis of software product lines

Software product line (SPL) engineering is a cost-effective approach to developing families of similar products. Key to the success of this approach is to correctly scope the domain of the SPL, identifying the common and variable features of the products and the interdependencies between features. In this study, the authors show how the commonality of a feature (i.e. the reuse ratio of the feature among the products) can be used to detect scope flaws in the early stages of development. SPL domains are usually modelled by means of feature diagrams following the feature-oriented domain analysis (FODA) notation. The authors extend classical FODA trees with unrestricted cardinalities, and present an algorithm to compute the number of products modelled by a feature diagram and the commonality of the features. Finally, the authors compare the performance of their algorithm with two other approaches built on top of boolean logic satisfiability (SAT)-solver technology such as cachet and relsat.

Safety analysis of software product lines using state-based modeling

The difficulty of managing variations and their potential interactions across an entire product line currently hinders safety analysis in safety-critical, software product lines. The work described here contributes to a solution by integrating product-line safety analysis with model-based development. This approach provides a structured way to construct state-based models of a product line having significant, safety-related variations and to systematically explore the relationships between behavioral variations and potential hazardous states through scenario-guided executions of the state model over the variations. The paper uses a product line of safety-critical medical devices to demonstrate and evaluate the technique and results.