Efficient static analysis and verification of featured transition systems

Formal semantics, modular specification, and symbolic verification of product-line behaviour

Software Product Lines (SPLs) are families of similar software products built from a common set of features. As the number of products of an SPL is potentially exponential in the number of its features, the model checking problem is harder than for single software. A practical way to face this exponential blow-up is to reuse common behaviour between products. We previously introduced Featured Transition Systems (FTS), a mathematical model that serves as a basis for efficient SPL model checking techniques. In this paper, we present ProVeLines, a product line of verifiers for SPLs that incorporates the results of over three years of research on formal verification of SPLs. Being itself a product line, our tool is flexible and extensible, and offers a wide range of solutions for SPL modelling and verification.

In a software product line (SPL), a collection of software products is defined by their commonalities in terms of features rather than explicitly specifying all products one-by-one. Several verification techniques were adapted to establish temporal properties of SPLs. Symbolic and family-based model checking have been proven to be successful for tackling the combinatorial blow-up arising when reasoning about several feature combinations. However, most formal verification approaches for SPLs presented in the literature focus on the static SPLs, where the features of a product are fixed and cannot be changed during runtime. This is in contrast to dynamic SPLs, allowing to adapt feature combinations of a product dynamically after deployment.

Software product lines (SPLs) are families of software systems sharing common assets and exhibiting variabilities specific to each product member of the family. Commonalities and variabilities are often represented as features organized in a feature model. Due to combinatorial explosion of the number of products induced by possible features combinations, exhaustive testing of SPLs is intractable. Therefore, sampling and prioritization techniques have been proposed to generate sorted lists of products based on coverage criteria or weights assigned to features. Solely based on the feature model, these techniques do not take into account behavioural usage of such products as a source of prioritization. In this paper, we assess the feasibility of integrating usage models into the testing process to derive statistical testing approaches for SPLs. Usage models are given as Markov chains, enabling prioritization of probable/rare behaviours. We used featured transition systems, compactly modelling variability and behaviour for SPLs, to determine which products are realizing prioritized behaviours. Statistical prioritization can achieve a significant reduction in the state space, and modelling efforts can be rewarded by better automation. In particular, we used MaTeLo, a statistical test cases generation suite developed at ALL4TEC. We assess feasibility criteria on two systems: Claroline, a configurable course management system, and Sferion™, an embedded system providing helicopter landing assistance.

Software product family or line is a software engineering paradigm that systematizes reuse. In software product line engineering, two phases are distinguished: domain engineering which is in charge of developing a common infrastructure and assets and application engineering which makes use of those assets to generate the products. One of the key aspects of product lines is variability and its management. However, the main focus has been on functional variability and quality variability in software product lines has not received so much attention by researchers. In a product line different members of the line may require different levels of a quality requirement, for instance they could differ in terms of their availability, security, reliability, etc. Due to this variability, quality evaluation in software product lines is much more complicated that in single-systems. One alternative is to evaluate all the products of a line but it is very expensive and ways of reducing evaluation efforts are necessary. In this direction, the paper presents a method for facilitating cost-effective quality evaluation of a product line taking into consideration variability on quality attributes.

This PhD project aims to automatically learn transition systems capturing the behaviour of a whole family of software-based systems. Reasoning at the family level yields important economies of scale and quality improvements for a broad range of systems such as software product lines, adaptive and configurable systems. Yet, to fully benefit from the above advantages, a model of the system family's behaviour is necessary. Such a model is often prohibitively expensive to create manually due to the number of variants. For large long-lived systems with outdated specifications or for systems that continuously adapt, the modelling cost is even higher. Therefore, this PhD proposes to automate the learning of such models from existing artefacts. To advance research at a fundamental level, our learning target are Featured Transition Systems (FTS), an abstract formalism that can be used to provide a pivot semantics to a range of variability-aware state-based modelling languages. The main research questions addressed by this PhD project are: (1) Can we learn variability-aware models efficiently? (2) Can we learn FTS in a black-box fashion? (i.e., with access to execution logs but not to source code); (3) Can we learn FTS in a white/grey-box testing fashion? (i.e., with access to source code); and (4) How do the proposed techniques scale in practice?

A software product line (SPL) usually involves a shared set of core assets and a series of application products. To ensure consistency, the evolution of the core assets and all the application products should be coordinated and synchronized under a unified evolution process. Therefore, SPL evolution often involves cross-product propagation and synchronization besides application derivation based on core assets, presenting quite different characteristic from the evolution of individual software products. As software architectures, including the product line architecture (PLA) and application architectures, play a central role in SPL engineering and evolution, architecture-based evolution analysis is a natural way for analyzing and managing SPL evolution. In this paper, we explore common practices of architecture evolution and the rationale behind in industrial SPL development. To this end, we conduct a case study with Wingsoft examination system product line (WES-PL), an industrial product line with an evolution history of eight years and more than 10 application products. In the case study, we reviewed the evolution history of WES-PL architecture and analyzed several typical evolution cases. Based on the historical analysis, we identify some special problems in industrial SPL practice from the aspect of architecture evolution and summarize some useful experiences about SPL evolution decisions to complement classical SPL methodology. On the other hand, we also propose some possible improvements for the evolution management in WES-PL.

Software product line (SPL) is a software engineering paradigm for software development. A software product within a product line often has specific functionalities that are not common to all other products within the product line. Those specific functionalities are termed “variant features” in a product line. SPL paradigm involves the modeling of variant features. However, little work in SPL investigates and addresses the modeling of variant features specific to user interface (UI). Unified Modeling Language (UML) is the de facto modeling language for object-oriented software systems. It is known that UML needs better support in modeling UIs. Thus, much research developed UML extensions to improve UML support in modeling UIs. Yet little of this work is related to developing such extensions for modeling UIs for SPLs in which variant features specific to UI modeling must be addressed. This research develops a UML extension -Web User Interface Modeling Language (WUIML) to address these problems. WUIML defines elements for modeling variant features specific to user interfaces for Web-based SPLs. The model elements in WUIML extend from the metaclass and BasicActivity of the UML2.0 metamodel. WUIML integrates the modeling of variant features specific to user interfaces to UML. For example, in a Web-based patient registration software product line, member products targeting British users may use British date format in the user interface, while member products targeting United States users may use United States date format in the user interface. Thus, this is a variant feature for this product line. WUIML defines a model element, XOR, to represent such exclusive or conditions in a product line user interface model. WUIML would reduce SPL engineers’ efforts needed in UI development. To validate the WUIML research outcome, a case study was conducted. The results of this empirical study indicate that modeling UIs for Web-based SPLs using WUIML is more effective and efficient than using standard UML.

International audience

A Software Product Line (SPL) is a set of systems that share a group of manageable features and satisfy the specific needs of a particular domain. The features of an SPL can be used in variable combinations to derive product variants in the SPL domain. Because SPLs promote product development through reuse, it is vital to have a means to measure their quality in terms of quality attributes like complexity, reusability,… In this paper, we propose a set of metrics to evaluate the quality of an SPL at three levels: the feature model, design and code. We adapted a set of metrics for software quality and defined new metrics to deal with the inherent characteristics of SPLs, specifically the feature model and the traceability between features, design and code. Furthermore, to assist in interpreting the quality of a given SPL, we conducted an empirical study over ten open source SPLs to identify thresholds for the proposed metrics.

Software Product Lines (SPL) are inherently difficult to test due to the\ncombinatorial explosion of the number of products to consider. To reduce the\nnumber of products to test, sampling techniques such as combinatorial\ninteraction testing have been proposed. They usually start from a feature model\nand apply a coverage criterion (e.g. pairwise feature interaction or\ndissimilarity) to generate tractable, fault-finding, lists of configurations to\nbe tested. Prioritization can also be used to sort/generate such lists,\noptimizing coverage criteria or weights assigned to features. However, current\nsampling/prioritization techniques barely take product behavior into account.\nWe explore how ideas of statistical testing, based on a usage model (a Markov\nchain), can be used to extract configurations of interest according to the\nlikelihood of their executions. These executions are gathered in featured\ntransition systems, compact representation of SPL behavior. We discuss possible\nscenarios and give a prioritization procedure illustrated on an example.\n

Software Product Lines (SPL) adoption can affect several aspects of an organization and it involves significant investment and risk. This way, SPL risk management is a crucial activity of SPL adoption. This study aims to identify SPL risks during the scoping and requirement disciplines to provide information to better understand risk management in SPL. In order to achieve the previous stated goal, a case study research was applied in an industrial project in the medical information management domain. Using the captured risks, a classification scheme was built and risk mitigation strategies were identified. We spent five months, totaling 79 hours, performing risk management (RM) in the scoping discipline and twelve months, totaling 148 hours, performing RM on the requirements discipline. We identified 32 risks during the scoping discipline and 20 risks during the requirements discipline, 14 risks occurred in both disciplines. Some identified risks are not particular to SPL development, however, they have their impact increased due to the SPL characteristic. All the study results and lessons learned are useful for all project managers and researchers who are considering the introduction of SPL risk management in industry or academia.

BackgroundThe software product lines (SPL) enable development teams to fully address a systematic reuse of shared assets to deliver a family of similar software products. Mobile applications are an obvious candidate for employing an SPL approach. This paper presents our research outcomes, based on empirical data from an industry-level development project. Two development teams were confronted with the same functionalities set to be delivered through a family of native mobile applications for Android and iOS.MethodsEmpirical data was gathered before, during and after a year of full-time development. The data demonstrate the impact of a SPL approach by comparing the SPL and non-SPL multiple edition development. One family of products (Android apps) was developed using an SPL approach, while another (iOS apps), functionally the same, was developed without employing an SPL approach. The project generated a volume of raw and aggregated empirical data to support our research questions.ResultsThe paper reports a positive impact of an SPL approach on product quality (internal and external) and feature output per week. As data shows, it also increases the delivery of functionalities (240% in 6 more editions), while investing the same amount of effort needed for a single-edition development. As a result of system-supported separation of development and production code, developers had a high confidence in further development. On the other hand, the second team delivered less new functionalities, only two new application editions, and lower software quality than the team that manages multi-edition development by employing an SPL approach.

A systematic mapping study on software product line evolution: From legacy system reengineering to product line refactoring

At the heart of product line development is the assumption that through structured reuse later products will be of a higher quality and require less time and effort to develop and test. This thesis presents empirical results from two case studies aimed at assessing the quality aspect of this claim and exploring fault prediction in the context of software product lines. The first case study examines pre-release faults and change proneness of four products in PolyFlow, a medium-sized, industrial software product line; the second case study analyzes post-release faults using pre-release data over seven releases of four products in Eclipse, a very large, open source software product line.;The goals of our research are (1) to determine the association between various software metrics, as well as their correlation with the number of faults at the component/package level; (2) to characterize the fault and change proneness of components/packages at various levels of reuse; (3) to explore the benefits of the structured reuse found in software product lines; and (4) to evaluate the effectiveness of predictive models, built on a variety of products in a software product line, to make accurate predictions of pre-release software faults (in the case of PolyFlow) and post-release software faults (in the case of Eclipse).;The research results of both studies confirm, in a software product line setting, the findings of others that faults (both pre- and post-release) are more highly correlated to change metrics than to static code metrics, and are mostly contained in a small set of components/ packages. The longitudinal aspect of our research indicates that new products do benefit from the development and testing of previous products. The results also indicate that pre-existing components/packages, including the common components/packages, undergo continuous change, but tend to sustain low fault densities. However, this is not always true for newly developed components/packages. Finally, the results also show that predictions of pre-release faults in the case of PolyFlow and post-release faults in the case of Eclipse can be done accurately from pre-release data, and furthermore, that these predictions benefit from information about additional products in the software product lines.