Berechnung von Attributsgrenzen für Teilkonfigurationen mit JavaSMT

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

Towards a Universal Variability Language: Master's Thesis

While feature diagrams have become the de facto standard to graphically describe variability models in Software Product Line Engineering (SPLE), none of the many textual notations have gained widespread adoption. However, a common textual language would be beneficial for better collaboration and exchange between tools. The main goal of this thesis is to propose a language for this purpose, along with fundamental tool support. The language should meet the needs and preferences of the community, so it can attain acceptance and adoption, without becoming yet another variability language. Its guiding principles are simplicity, familiarity, and flexibility. These enable the language to be easy to learn and to integrate into different tools, while still being expressive enough to represent existing and future models. We incorporate general design principles for Domain-Specific Languages (DSLs), discuss usage scenarios collected by the community, analyze existing languages, and gather feedback directly through questionnaires submitted to the community. In the initial questionnaire, the community was in disagreement on whether to use nesting or references to represent the hierarchy. Thus, we presented two proposals to be compared side by side. Of those, the community clearly prefers the one using nesting, as determined by a second questionnaire. We call that proposal the Universal Variability Language (UVL). The community awards good ratings to this language, deems it suitable for teaching and learning, and estimates that it can represent most of the existing models. Evaluations reconsidering the requirements show that it enables the relevant scenarios and can support the editing of large-scale real-world feature models, such as the Linux kernel. We provide a small default library that can be used in Java, containing a parser and a printer for the language. We integrated it into the variability tool FeatureIDE, demonstrating its utility in quickly adding support for the proposed language. Overall, we can conclude that UVL is well-suited for a base language level of a universal textual variability language. Along with the acquired insights into the requirements for such a language, it can pose as the basis for the SPLE community to commit to a common language. As exchange and collaboration would be simplified, higher-quality research could be conducted and better tools developed, serving the whole community

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

Lazy product discovery in huge configuration spaces

Highly-configurable software systems can have thousands of inter-dependent configuration options across different subsystems. In theresulting configuration space, discovering a valid product configu-ration for some selected options can be complex and error prone.The configuration space can be organized using a feature model,fragmented into smaller interdependent feature models reflectingthe configuration options of each subsystem.We propose a method for lazy product discovery in large frag-mented feature models with interdependent features. We formalizethe method and prove its soundness and completeness. The evalu-ation explores an industrial-size configuration space. The resultsshow that lazy product discovery has significant performance ben-efits compared to standard product discovery, which in contrastto our method requires all fragments to be composed to analyzethe feature model. Furthermore, the method succeeds when moreefficient, heuristics-based engines fail to find a valid configuration

Konsistente Feature Modell gesteuerte Softwareproduktlinien Evolution

SPLs are an approach to manage families of closely related software systems in terms of configurable functionality. A feature model captures common and variable functionalities of an SPL on a conceptual level in terms of features. Reusable artifacts, such as code, documentation, or tests are related to features using a feature-artifact mapping. A product of an SPL can be derived by selecting features in a configuration. Over the course of time, SPLs and their artifacts are subject to change. As SPLs are particularly complex, their evolution is a challenging task. Consequently, SPL evolution must be thoroughly planned well in advance. However, plans typically do not turn out as expected and, thus, replanning is required. Feature models lean themselves for driving SPL evolution. However, replanning of feature-model evolution can lead to inconsistencies and feature-model anomalies may be introduced during evolution. Along with feature-model evolution, other SPL artifacts, especially configurations, need to consistently evolve. The work of this thesis provides remedy to the aforementioned challenges by presenting an approach for consistent evolution of SPLs. The main contributions of this thesis can be distinguished into three key areas: planning and replanning feature-model evolution, analyzing feature-model evolution, and consistent SPL artifact evolution. As a starting point for SPL evolution, we introduce Temporal Feature Models (TFMs) that allow capturing the entire evolution timeline of a feature model in one artifact, i.e., past history, present changes, and planned evolution steps. We provide an execution semantics of feature-model evolution operations that guarantees consistency of feature-model evolution timelines. To keep feature models free from anomalies, we introduce analyses to detect anomalies in feature-model evolution timelines and explain these anomalies in terms of their causing evolution operations. To enable consistent SPL artifact evolution, we generalize the concept of modeling evolution timelines in TFMs to be applicable for any modeling language. Moreover, we provide a methodology that enables involved engineers to define and use guidance for configuration evolution.Softwareproduktlinien (SPLs) ermöglichen es, konfigurierbare Funktionalität von eng verwandten Softwaresystemen zu verwalten. In einem Feature Modell werden gemeinsame und variable Funktionalitäten einer SPL auf Basis abstrakter Features modelliert. Wiederverwendbare Artefakte werden in einem Feature-Artefakt Mapping Features zugeordnet. Ein Produkt einer SPL kann abgeleitet werden, indem Features in einer Konfiguration ausgewählt werden. Im Laufe der Zeit müssen sich SPLs und deren Artefakte verändern. Da SPLs ganze Softwarefamilien modellieren, ist deren Evolution eine besonders herausfordernde Aufgabe, die gründlich im Voraus geplant werden muss. Feature Modelle eignen sich besonders als Planungsmittel einer SPL. Umplanung von Feature Modell Evolution kann jedoch zu Inkonsistenzen führen und Feature Modell Anomalien können im Zuge der Evolution eingeführt werden. Im Anschluss an die Feature Modell Evolution muss die Evolution anderer SPL Artefakte, insbesondere Konfigurationen, konsistent modelliert werden. In dieser Arbeit wird ein Ansatz zur konsistenten Evolution von SPLs vorgestellt, der die zuvor genannten Herausforderungen adressiert. Die Beiträge dieser Arbeit lassen sich in drei Kernbereiche aufteilen: Planung und Umplanung von Feature Modell Evolution, Analyse von Feature Modell Evolution und konsistente Evolution von SPL Artefakten. Temporal Feature Models (TFMs) werden als Startpunkt für SPL Evolution eingeführt. In einem TFM wird die gesamte Evolutionszeitlinie eines Feature Modells in einem Artefakt abgebildet, was sowohl vergangene Änderungen, den aktuellen Zustand, als auch geplante Änderungen beinhaltet. Auf Basis einer Ausführungssemantik wird die Konsistenz von Feature Modell Evolutionszeitlinien sichergestellt. Um Feature Modelle frei von Anomalien zu halten, werden Analysen eingeführt, welche die gesamte Evolutionszeitlinie eines Feature Modells auf Anomalien untersucht und diese mit verursachenden Evolutionsoperationen erklärt. Das Konzept zur Modellierung von Feature Modell Evolutionszeitlinien aus TFMs wird verallgemeinert, um die gesamte Evolution von Modellen beliebiger Modellierungssprachen spezifizieren zu können. Des Weiteren wird eine Methodik vorgestellt, die beteiligten Ingenieuren eine geführte Evolution von Konfigurationen ermöglicht

Domain Specific Languages for Managing Feature Models: Advances and Challenges

International audienceManaging multiple and complex feature models is a tedious and error-prone activity in software product line engineering. Despite many advances in formal methods and analysis techniques, the supporting tools and APIs are not easily usable together, nor unified. In this paper, we report on the development and evolution of the Familiar Domain-Specific Language (DSL). Its toolset is dedicated to the large scale management of feature models through a good support for separating concerns, composing feature models and scripting manipulations. We overview various applications of Familiar and discuss both advantages and identified drawbacks. We then devise salient challenges to improve such DSL support in the near future

A modular metamodel and refactoring rules to achieve software product line interoperability.

Emergent application domains, such as cyber–physical systems, edge computing or industry 4.0. present a high variability in software and hardware infrastructures. However, no single variability modeling language supports all language extensions required by these application domains (i.e., attributes, group cardinalities, clonables, complex constraints). This limitation is an open challenge that should be tackled by the software engineering field, and specifically by the software product line (SPL) community. A possible solution could be to define a completely new language, but this has a high cost in terms of adoption time and development of new tools. A more viable alternative is the definition of refactoring and specialization rules that allow interoperability between existing variability languages. However, with this approach, these rules cannot be reused across languages because each language uses a different set of modeling concepts and a different concrete syntax. Our approach relies on a modular and extensible metamodel that defines a common abstract syntax for existing variability modeling extensions. We map existing feature modeling languages in the SPL community to our common abstract syntax. Using our abstract syntax, we define refactoring rules at the language construct level that help to achieve interoperability between variability modeling languages.Work supported by the projects MEDEA RTI2018-099213-B-I00, IRIS PID2021-122812OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/FEDER, UE), LEIA UMA18-FEDERIA-157, and DAEMON H2020-101017109. // Funding for open access: Universidad de Málaga / CBUA

Uniform Random Sampling Product Configurations of Feature Models That Have Numerical Features

Analyses of Software Product Lines (SPLs) rely on automated solvers to navigate complex dependencies among features and find legal configurations. Often these analyses do not support numerical features with constraints because propositional formulas use only Boolean variables. Some automated solvers can represent numerical features natively, but are limited in their ability to count and Uniform Random Sample (URS) conigurations, which are key operations to derive unbiased statistics on configuration spaces. Bit-blasting is a technique to encode numerical constraints as propositional formulas. We use bit-blasting to encode Boolean and numerical constraints so that we can exploit existing #SAT solvers to count and URS conigurations. Compared to state-of-art Satisfiability Modulo Theory and Constraint Programming solvers, our approach has two advantages: 1) faster and more scalable coniguration counting and 2) reliable URS of SPL configurations. We also show that our work can be used to extend prior SAT-based SPL analyses to support numerical features and constraints.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech