Analysis of Feature Models Using Alloy: A Survey

Feature Models (FMs) are a mechanism to model variability among a family of closely related software products, i.e. a software product line (SPL). Analysis of FMs using formal methods can reveal defects in the specification such as inconsistencies that cause the product line to have no valid products. A popular framework used in research for FM analysis is Alloy, a light-weight formal modeling notation equipped with an efficient model finder. Several works in the literature have proposed different strategies to encode and analyze FMs using Alloy. However, there is little discussion on the relative merits of each proposal, making it difficult to select the most suitable encoding for a specific analysis need. In this paper, we describe and compare those strategies according to various criteria such as the expressivity of the FM notation or the efficiency of the analysis. This survey is the first comparative study of research targeted towards using Alloy for FM analysis. This review aims to identify all the best practices on the use of Alloy, as a part of a framework for the automated extraction and analysis of rich FMs from natural language requirement specifications.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

Derivation of subset product lines in FeatureIDE

The development and configuration of software product lines can be challenging tasks. During development, engineers often need to focus on a particular subset of features that is relevant for them. In such cases, it would be beneficial to hide other features and their implementation. During product configuration, requirements of potentially multiple stakeholders need to be considered. Therefore, configuration often happens in stages, in which different people contribute configuration decisions for different features. Moreover, in some cases, stakeholders want to share a set of products rather than a specific one. In all these cases, the necessary operation is the same: some features from the product line are assigned a value (e.g., via a partial configuration) while other features remain configurable. In this work, we propose a subset operation that takes a product line and a partial configuration to derive a subset product line comprising only the desired subset of features and implementation artifacts. Furthermore, we present, evaluate, and publish our implementation of the proposed subset operation within the FeatureIDE framework

Mobile Media SPL creation by Feature IDE using FODA

Software Product Lines are used in many areas, combining to form new technologies and products. A product line is a group of products that share a common development platform and vary by the composition and implementation method for the functionalities. This paper describes the implementation or creation of MobileMedia feature model using FODA (Feature Oriented Domain Analysis) methodology using FeatureIDE eclipse plug-in. The feature model created in this depicts various outlines as feature model as visual model, collaboration diagram view of model, its configuration, FeatureIDE Statistics. Basically the paper shows the concept how SPLs can be viewed as feature diagrams using various tools in order to deal with them. This modelling has been widely used by software product line communities and a number of extensions have been proposed

Software product line engineering: a practical experience

The lack of mature tool support is one of the main reasons that make the industry to be reluctant to adopt Software Product Line (SPL) approaches. A number of systematic literature reviews exist that identify the main characteristics offered by existing tools and the SPL phases in which they can be applied. However, these reviews do not really help to understand if those tools are offering what is really needed to apply SPLs to complex projects. These studies are mainly based on information extracted from the tool documentation or published papers. In this paper, we follow a different approach, in which we firstly identify those characteristics that are currently essential for the development of an SPL, and secondly analyze whether the tools provide or not support for those characteristics. We focus on those tools that satisfy certain selection criteria (e.g., they can be downloaded and are ready to be used). The paper presents a state of practice with the availability and usability of the existing tools for SPL, and defines different roadmaps that allow carrying out a complete SPL process with the existing tool support.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Magic P12-TIC1814, HADAS TIN2015-64841-R (cofinanciado con fondos FEDER), MEDEA RTI2018-099213-B-I00 (cofinanciado con fondos FEDER), TASOVA MCIU-AEI TIN2017-90644-RED

The Kconfig Variability Framework as a Feature Model

Zur einfachen Handhabung von Softwarevariabilität werden oft externe Werkzeuge eingesetzt. Ein solches Werkzeug ist Kconfig, welches vom Linux-Kernel zur Erstellung von konkreten Softwarekonfigurationen benutzt wird. Kconfig arbeitet mit Textdateien, in denen die Variabilitätsstruktur des zugehörigen Softwareprojekts definiert wird. Diese Dateien werden oft als Kconfig-Dateien bezeichnet. Kconfig-Dateien können analysiert werden, um Probleme in der Variabilitätsstruktur festzustellen. Feature-orientierte Programmierung (FOP) wird auch zur besseren Handhabung von Softwarevariabilität eingesetzt. Die Variabilitätsstruktur eines Softwareprojekts wird im Umfang von FOP in einem sogenannten Feature-Modell dargestellt. Es gibt Werkzeuge, welche zur Analyse von Feature-Modellen verwendet werden können. Diese kann man jedoch nicht zur Analyse von Kconfig-Dateien nutzen, da bisher eine Transformation zwischen Kconfig-Dateien und Feature-Modellen fehlt. In dieser Arbeit stellen wir eine Methodik zur korrekten Transformation von Kconfig-Dateien in Feature-Modelle vor, sodass Werkzeuge zur Feature-Modell-Analyse auch auf Kconfig-Dateien angewandt werden können. Wir evaluieren die Korrektheit unserer Transformation mit automatischen und manuellen Vorgehen. Unsere Methodik kann ausgewählte Kconfig-Dateien mit nichttrivialer Struktur erfolgreich in semantisch äquivalente Feature-Modelle überführen

Anwendungen von #SAT Solvern für Produktlinien: Masterarbeit

Product lines are widely used for managing families of similar products. Typically, product lines are complex and infeasible to analyze manually. In the last two decades, product-line analyses have been reduced to satisfiability problems which are well understood. However, there are methods for which satisfiability is not sufficient. Recently, researchers begun to reduce other problems to #SAT. Yet, only few applications have been considered and those are fairly limited in their scope. Furthermore, the authors mainly propose ad-hoc solutions that are only applicable under certain restrictions or do not scale to large product lines. In this thesis, we aim show the benefits of applying #SAT for the analysis of product lines. To this end, we make the following contributions: First, we summarize applications dependent on #AT considered in the literature and propose new applications to motivate the usage of #SAT technology. Second, we present a variety of algorithms and optimizations for these applications including new proposals. Third, we empirically evaluate 10 proposed algorithms with 14 off-the-shelf #SAT solvers on 131 industrial feature models to identify the fastest algorithms and solvers. Our results show that for each analysis at least one algorithm and solver scale on a vast majority of the feature models, whereas Linux and an automotive model not be analyzed at all. In addition, our results further reveal the benefits of knowledge compilation to deterministic decomposable negation normal form for performing counting-based analyses. Overall, our work shows that #SAT dependent analyses for feature models open a new variety of different applications and scale to a large number of industrial feature models.Produktlinien sind weit verbreitet für die Verwaltung von Familien verwandter Pro- dukte. In der Regel sind Produktlinien komplex und manuell schwer zu analysieren. In den letzten zwei Jahrzehnten wurden Produktlinienanalysen auf Erfüllbarkeit- sprobleme reduziert, für welche es eine Vielzahl an effizienten Werkzeugen gibt. Allerdings ist Erfüllbarkeit nicht für alle Analysen hinreichend. Kürzlich haben Forscher damit begonnen, andere Probleme auf #SAT zu reduzieren. Es wur- den jedoch nur wenige Anwendungen in Betracht gezogen und auch der Anwen- dungsbereich ist begrenzt. Darüber hinaus schlagen die Autoren hauptsächlich Ad-hoc-Lösungen vor, die nur unter bestimmten Einschränkungen der Produktlin- ien anwendbar sind oder nicht für große Produktlinien skalieren. In dieser Arbeit zeigen wir die Vorteile von #SAT Anwendungen für Produtlinien auf. Unser wis- senschaftlicher Beitrag besteht aus den folgenden drei Punkten: Zuerst fassen wir die in der Literatur betrachteten #SAT-Anwendungen zusammen und schlagen neue Anwendungen vor, um den Einsatz von #SAT-Technologien zu motivieren. Zweit- ens stellen wir eine Vielzahl von Algorithmen und Optimierungen für diese Anwen- dungen vor, einschließlich neuer Vorschläge. Drittens führen wir eine empirische Evaluation von 10 der vorgeschlagenen Algorithmen mit 14 #SAT-Solvern auf 131 industriellen Feature-Modellen aus, um die schnellsten Algorithmen und Solver zu identifizieren. Die Ergebnisse der Evaluation zeigen, dass wir für jede Analyse wenig- stens einen Algorithmus und Solver identifiziert haben, die für industrielle Feature- Modelle skalieren. Dazu sind die Ergebnisse ein starker Indikator für die Vorteile des Einsatzes von d-DNNFs bei #SAT-Anwendungen. Insgesamt zeigt unsere Ar- beit, dass #SAT-abhängige Analysen für Feature-Modelle eine Vielzahl neuer un- terschiedlicher Anwendungen ermöglicht und für viele industirelle Feature-Modelle skaliert

FM fact label: a configurable and interactive visualization of feature model characterizations

Recognizing specific characteristics of feature models (FM) can be challenging due to the different nature and domains of the models. There are several metrics to characterize FMs. However, there is no standard way to visualize and identify the properties that make an FM unique and distinguishable. We propose FM Fact Label as a tool to visualize an FM characterization based on its metadata, structural measures, and analytical metrics. Although existing tools can provide a visualization of the FM and report some metrics, the feature diagram of large-scale FMs becomes ineffective to take an overall shape of the FM properties. Moreover, the reported metrics are often embedded in the tool user interface, preventing further analysis. FM Fact Label is a standalone web-based tool that provides a configurable and interactive visualization of FM characterizations that can be exported to several formats. Our contribution becomes important because the Universal Variability Language (UVL) is starting to gain attraction in the software product line community as a unified textual language to specify FMs and share knowledge. With this contribution, we help to advance the UVL ecosystem one step forward while providing a common representation for the results of existing analysis toolsMinisterio de Ciencia, Innovación y Universidades RTI2018-101204-B-C22 (OPHELIA)Junta de Andalucía P20-01224 (COPERNICA)Junta de Andalucía METAMORFOSIS (US-1381375)Ministerio de Economía y Competitividad RTI2018-099213-B-I00 (MEDEA)Ministerio de Ciencia e Innovación PID2021-122812OB-I00 (IRIS)Junta de Andalucía P18-FR-1081 (RHEA)Junta de Andalucía UMA18-FEDERIA-157 (LEIA)European Union H2020 101017109 (DAEMON

Sampling-Strategien zur Erzeugung von Szenarien für die simulationsbasierte Validierung von Fahrerassistenzsystemen

Scenario-based testing is a common approach to verify and validate Advanced Driving Assistance System / Autonomous Driving (ADAS/AD) of motor vehicles. The main challenge in scenario-based testing is the selection of a finite number of scenarios to represent an infinite amount of possible scenarios. Beyond that, there is no metric to evaluate scenarios thus the quality of the testing process. We introduce a generic process chain to ensure traceability and reproducibility of scenario selection, by generating scenarios automatically. A Feature Model (FM) builds the input data for our process chain. We identify three concepts to represent a scenario using a FM. We create a tool to transfer a configuration of the FM into a concrete scenario. A sample represents a set of scenarios, we define them as scenario suite. We evaluate the quality of a scenario suite by applying its scenarios to various mutants of driving functions in a simulation tool. The quality of the scenario suites is then determined by the number of discovered mutants. We evaluate the influence of various FMs in combination with common sampling algorithms such as ICPL, Chvatal, and IncLing, using an Autonomous Emergency Braking (AEB) as subject system. We discover a correlation between FM and mutation score as well as between mutation score and sampling algorithm. Within a scenario suite, we identify a strict separation between scenarios that are good to kill a mutant and those which are not. We discover, that sampling algorithms that aim for feature interaction coverage produce stronger scenario suites than feature-wise sampling algorithms. An evaluation of the relevance of single features on the mutation score provides features that are frequently involved in scenarios that are good to kill mutants. Beyond that, we discover a correlation between scenario suite and mutants that affects the mutant detection