Prediction Algorithms and Consecutive Estimation of Software Product Line Feature Model Usability

Needs for mass customization and economies of scale have pushed engineers to develop Software Product Lines (SPLs). SPLs are families of products sharing commonalities and exhibiting differences, built by reusing software assets abstractly represented by features. Feature models describe the constraints that link the features and allow the configuration of tailored software products. Common SPLs involve hundreds, even thousands of features, leading to billions of possible software products. As a result, testing a product line is challenging due to the enormous size of the possible products. Existing techniques focus on testing based on the product line's feature model by selecting a limited set of products to test. Being created manually or reverse-engineered, feature models are prone to errors impacting the generated test suites. In this paper, we examine ability of test suites to detect such errors. In particular, we propose two mutation operators to derive erroneous feature models (mutants) from an original feature model and assess the capability of the generated original test suite to kill the mutants. Experimentation on real feature models demonstrate that dissimilar tests suites have a higher mutant detection ability than similar ones, thus validating the relevance of similarity-driven product line testing.

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.

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.

Feature models are widely used for describing the variability of a software product line. A feature model contains a tree of features and a set of constraints over these features, which define valid feature combinations. In the industrial practice, large feature models containing hundreds of features and constraints are common. Furthermore, in a hierarchical product line a feature model can be related to other feature models through inter-model constraints. Due to the model size and complexity, understanding industrial feature models is a challenging task.In this paper, we describe the feature model understanding challenges reported by feature model developers at Robert Bosch GmbH. To support the developers in model understanding, we extend the idea of a feature implication graph to feature relation graph by abstracting groups of implications to feature relations. A transitively closed relation graph shows all modeled and implicit feature relations and spans all related feature models. The graph is also used to identify modeling problems, such as false optional or dead features, and to show the derivation of any implicit relation or problem from the modeled constraints. In a case study at Bosch, we evaluate the use of feature relation graph for model understanding. We propose further use cases of the graph, supporting model maintenance, evolution and configuration.

Software product lines are able to describe multiple products sharing a common base of features and are commonly described as feature models. For complex software product lines, automatic analyses are required to ensure validity and to improve the interactive configuration process. Modern SAT solvers are vital components for the validation process of feature models. The increasing variability of software product lines implies the need to use more expressive solvers like SMT solvers. To assist the development of feature modeling tools, we compare SAT and SMT solvers for the automated analysis of feature models. During this thesis, we create an abstract data type to formally define analyses for feature model defects and their explanations. The result shows that SAT solvers are more efficient at detecting the defects, while SMT solvers can find explanations for them multiple times faster. Feature models can be further expanded by attaching attributes to features. Such attributes may contain a numerical value. Additionally, one attribute can be defined for multiple features. In this thesis, we aim to support the interactive configuration process, by providing the range of the sum of values for an attribute. Such ranges depend on the remaining choices in a configuration of the product line. We provide an exact computation using SMT and an approximation using a heuristic. The evaluation results show that an SMT solver is not suitable for supporting interactive configuration. However, the approximated ranges of the provided heuristic were very close to the exact ones.

Lately, the demand for mobile applications development has increased significantly mainly due to growth of use of mobile devices and the need to port existing web applications. To reduce development’s time and cost, Software Product Lines (SPLs) have also been used in the context of mobile applications. However, the existing SPLs do not worry about supporting the development of mobile applications corresponding to the existing Web applications, as it is desirable to have access to the information and main features of these applications in mobile devices. In face of this problem, this paper discusses the motivation and presents the evolution from a SPL in the e-Gov Web (e-Gov Web SPL) domain to a SPL in the mobile domain (e-Gov Mobile SPL) having in mind the need to supply market demand. The conducted evolution was supported by the PLUS approach (Product Line UML-Based Software Engineering) and by the features model. Furthermore, this work debates the main results obtained through some e-Gov Mobile SPL instantiations in the precision livestock domain.KeywordsSoftware Product LineEvolutione-GovWebMobile

Automating feature model refactoring: A Model transformation approach

Trusted Product Lines

For the development of variable systems, software product lines (SPL) are an established way to handle the variability by using feature models. Nevertheless, the configuration of an SPL can be complex, especially if a product line consists of a large number of features. The problem of handling the complexity becomes even more sophisticated if not only software, but also mechatronic systems containing software and hardware components are configured. Besides modeling the software, within a mechatronic system dependencies and associations between software and hardware features need to be considered which further increases the complexity. To handle this complexity in product lines for mechatronic systems, we propose a multi product line (MPL) approach which allows to distinguish between software and hardware by using different feature models for each. In addition we introduce a level of abstraction to complex product lines consisting of multiple feature models by establishing a feature model mapping. In this paper we present details to the mapping to provide an abstract configuration view as well as the introduced associations for our MPL approach.

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.

Automated diagnosis of feature model configurations

Feature models are commonly used to specify variability in software product lines. Several tools support feature models for variability management at different steps in the development process. However, tool support for test configuration generation is currently limited. This test generation task consists in systematically selecting a set of configurations that represent a relevant sample of the variability space and that can be used to test the product line. In this paper we propose \pw tool to analyze feature models and automatically generate a set of configurations that cover all pair wise interactions between features. \pw tool relies on constraint programming to generate configurations that satisfy all constraints imposed by the feature model and to minimize the set of the tests configurations. This work also proposes an extensive experiment, based on the state-of-the art SPLOT feature models repository, showing that \pw tool scales over variability spaces with millions of configurations and covers pair wise with less configurations than other available tools.

Software Product Line Engineering aims to ensure the correctness, completeness and consistency among its artifacts and the specified domain, in order to prevent the spread of defects for the products derived from this domain. Among initial artifacts of a software product line, feature models are artifacts generated in various domain engineering approaches. Although software inspection is highlighted as an effective review activity for detection of defects in software artifacts, mainly in the early models of software projects, a recent quasi-systematic review of literature indicated a lack of techniques to support the inspection of software product line artifacts, which include features models. Thus, this paper presents FMCheck, a checklist-based inspection technique to support the detection of defects on feature models. This technique was developed to be configurable and to be applied on several extensions of the original feature model notation presented by FODA approach, including the Odyssey-FEX notation, in particular. FMCheck was submitted to a proof of concept and to a further in vitro feasibility study, in which it could be seen the feasibility of FMCheck application and also that inspections applying FMCheck are more effective than ad-hoc inspections, when applied on four distinct domains.

Goal models can capture similarities and the variability of a Software Product Line (SPL), but they cannot describe the detailed behavior of the SPL functionality. Due to this limitation, a process called GS2SPL was defined to systematically obtain, from goal models, feature models and the specification of use case scenarios with variability. However, the variability and the configuration knowledge of the SPL are tangled in the scenarios description, jeopardizing the maintenance and reuse of this artifact. In order to handle with this problem, it was proposed a technique called MSVCM. It specifies the SPL variability and the configuration knowledge separately from the common behavior present in the scenarios, as well as it defines a process to configure the specification of a product. However, there is a lack of traceability between the MSVCM scenarios and the goals and quality that the stakeholder expects to achieve. Thus, this work proposes to obtain, systematically, the specification of MSVCM scenarios from goal models. Using goals in SPL allows a systematic derivation of the SPL requirements from these goals. This new approach is called GAS2SPL and it is illustrated by using the TaRGeT example.

With the growing academic and industrial interest in Software Product Lines, one area demanding special attention is tool support development, which is a pre-requisite for widespread software product lines practices adoption. In this demo, we present FLiP, a suite of tools consisting of 3 modules: a refactoring tool that implements code transformations for extracting product variations from Java classes into AspectJ aspects, a module that integrates with a Feature Model tool for updating a software product lines feature model accordingly to code transformations, and a tool responsible for building the final products. FLiP has been designed and tested in the context of industrial-strength mobile game software product lines.