Rick Rabiser

Cool features and tough decisions: a comparison of variability modeling approaches

Variability modeling is essential for defining and managing the commonalities and variabilities in software product lines. Numerous variability modeling approaches exist today to support domain and application engineering activities. Most are based on feature modeling (FM) or decision modeling (DM), but so far no systematic comparison exists between these two classes of approaches. Over the last two decades many new features have been added to both FM and DM and it is tough to decide which approach to use for what purpose. This paper clarifies the relation between FM and DM. We aim to systematize the research field of variability modeling and to explore potential synergies. We compare multiple aspects of FM and DM ranging from historical origins and rationale, through syntactic and semantic richness, to tool support, identifying commonalities and differences. We hope that this effort will improve the understanding of the range of approaches to variability modeling by discussing the possible variations. This will provide insights to users considering adopting variability modeling in practice and to designers of new languages, such as the new OMG Common Variability Language.

The DOPLER meta-tool for decision-oriented variability modeling: a multiple case study

The variability of a product line is typically defined in models. However, many existing variability modeling approaches are rigid and don’t allow sufficient domain-specific adaptations. We have thus been developing a flexible and extensible approach for defining product line variability models. Its main purposes are to guide stakeholders through product derivation and to automatically generate product configurations. Our approach is supported by the DOPLER (Decision-Oriented Product Line Engineering for effective Reuse) meta-tool that allows modelers to specify the types of reusable assets, their attributes, and dependencies for their specific system and context. The aim of this paper is to investigate the suitability of our approach for different domains. More specifically, we explored two research questions regarding the implementation of variability and the utility of DOPLER for variability modeling in different domains. We conducted a multiple case study consisting of four cases in the domains of industrial automation systems and business software. In each of these case studies we analyzed variability implementation techniques. Experts from our industry partners then developed domain-specific meta-models, tool extensions, and variability models for their product lines using DOPLER. The four cases demonstrate the flexibility of the DOPLER approach and the extensibility and adaptability of the supporting meta tool.

Supporting Product Derivation by Adapting and Augmenting Variability Models

Product derivation is the process of constructing products from the core assets in a product line. Guidance and support are needed to increase efficiency and to deal with the complexity of product derivation. Research has, however, devoted comparatively little attention to this process. In this paper we describe an approach for supporting product derivation. We show that variability models need to be prepared for concrete projects before they can be effectively utilized in the derivation process. Project-specific information and sales knowledge should be added and irrelevant variability should be pruned. We also present tool support and illustrate the approach using examples from ongoing research collaboration.

Software diversity: state of the art and perspectives

Diversity is prevalent in modern software systems to facilitate adapting the software to customer requirements or the execution environment. Diversity has an impact on all phases of the software development process. Appropriate means and organizational structures are required to deal with the additional complexity introduced by software variability. This introductory article to the special section “Software Diversity—Modeling, Analysis and Evolution” provides an overview of the current state of the art in diverse systems development and discusses challenges and potential solutions. The article covers requirements analysis, design, implementation, verification and validation, maintenance and evolution as well as organizational aspects. It also provides an overview of the articles which are part of this special section and addresses particular issues of diverse systems development.

Requirements for product derivation support: Results from a systematic literature review and an expert survey

Context: An increasing number of publications in product line engineering address product derivation, i.e., the process of building products from reusable assets. Despite its importance, there is still no consensus regarding the requirements for product derivation support. Objective: Our aim is to identify and validate requirements for tool-supported product derivation. Method: We identify the requirements through a systematic literature review and validate them with an expert survey. Results: We discuss the resulting requirements and provide implementation examples from existing product derivation approaches. Conclusions: We conclude that key requirements are emerging in the research literature and are also considered relevant by experts in the field.

A comparison of decision modeling approaches in product lines

It has been shown that product line engineering can significantly improve the productivity, quality and time-to-market of software development by leveraging extensive reuse. Variability models are currently the most advanced approach to define, document and manage the commonalities and variabilities of reusable artifacts such as software components, requirements, test cases, etc. These models provide the basis for automating the derivation of new products and are thus the key artifact to leverage the flexibility and adaptability of systems in a product line. Among the existing approaches to variability modeling feature modeling and decision modeling have gained most importance. A significant amount of research exists on comparing and analyzing different feature modeling approaches. However, despite their significant role in product line research and practical applications, only little effort has been devoted to compare and analyze decision modeling approaches. In order to address this shortcoming and to provide a basis for more structured research on decision modeling in the future, we present a comparative analysis of representative approaches. We identify their major modeling concepts and present an analysis of their commonalities and variabilities.

Structuring the modeling space and supporting evolution in software product line engineering

The scale and complexity of product lines means that it is practically infeasible to develop a single model of the entire system, regardless of the languages or notations used. The dynamic nature of real-world systems means that product line models need to evolve continuously to meet new customer requirements and to reflect changes of product line artifacts. To address these challenges, product line engineers need to apply different strategies for structuring the modeling space to ease the creation and maintenance of models. This paper presents an approach that aims at reducing the maintenance effort by organizing product lines as a set of interrelated model fragments defining the variability of particular parts of the system. We provide support to semi-automatically merge fragments into complete product line models. We also provide support to automatically detect inconsistencies between product line artifacts and the models representing these artifacts after changes. Furthermore, our approach supports the co-evolution of models and their respective meta-models. We discuss strategies for structuring the modeling space and show the usefulness of our approach using real-world examples from our ongoing industry collaboration.

Agile product line planning: A collaborative approach and a case study

Agile methods and product line engineering (PLE) have both proven successful in increasing customer satisfaction and decreasing time to market under certain conditions. Key characteristics of agile methods are lean and highly iterative development with a strong emphasis on stakeholder involvement. PLE leverages reuse through systematic approaches such as variability modeling or product derivation. Integrating agile approaches with product line engineering is an interesting proposition which - not surprisingly - entails several challenges: Product lines (PL) rely on complex plans and models to ensure their long-term evolution while agile methods emphasize simplicity and short-term value-creation for customers. When incorporating agility in product line engineering, it is thus essential to define carefully how agile principles can support particular PLE processes. For instance, the processes of defining and setting up a product line (domain engineering) and deriving products (application engineering) differ significantly in practices and focus with implications on the suitability of agile principles. This paper presents practical experiences of adopting agile principles in product line planning (a domain engineering activity). ThinkLets, i.e., collaborative practices from the area of collaboration engineering, are the building blocks of the presented approach as they codify agile principles such as stakeholder involvement, rapid feedback, or value-based prioritization. We discuss how our approach balances agility and the intrinsic needs of product line planning. A case study carried out with an industrial partner indicates that the approach is practicable, usable, and useful.

A systematic review and an expert survey on capabilities supporting multi product lines

Context: Complex software-intensive systems comprise many subsystems that are often based on heterogeneous technological platforms and managed by different organizational units. Multi product lines (MPLs) are an emerging area of research addressing variability management for such large-scale or ultra-large-scale systems. Despite the increasing number of publications addressing MPLs the research area is still quite fragmented. Objective: The aims of this paper are thus to identify, describe, and classify existing approaches supporting MPLs and to increase the understanding of the underlying research issues. Furthermore, the paper aims at defining success-critical capabilities of infrastructures supporting MPLs. Method: Using a systematic literature review we identify and analyze existing approaches and research issues regarding MPLs. Approaches described in the literature support capabilities needed to define and operate MPLs. We derive capabilities supporting MPLs from the results of the systematic literature review. We validate and refine these capabilities based on a survey among experts from academia and industry. Results: The paper discusses key research issues in MPLs and presents basic and advanced capabilities supporting MPLs. We also show examples from research approaches that demonstrate how these capabilities can be realized. Conclusions: We conclude that approaches supporting MPLs need to consider both technical aspects like structuring large models and defining dependencies between product lines as well as organizational aspects such as distributed modeling and product derivation by multiple stakeholders. The identified capabilities can help to build, enhance, and evaluate MPL approaches.

Flexible and scalable consistency checking on product line variability models

The complexity of product line variability models makes it hard to maintain their consistency over time regardless of the modeling approach used. Engineers thus need support for detecting and resolving inconsistencies. We describe experiences of applying a tool-supported approach for incremental consistency checking on variability models. Our approach significantly improves the overall performance and scalability compared to batch-oriented techniques and allows providing immediate feedback to modelers. It is extensible as new consistency constraints can easily be added. Furthermore, the approach is flexible as it is not limited to variability models and it also checks the consistency of the models with the underlying code base of the product line. We report the results of a thorough evaluation based on real-world product line models and discuss lessons learned.