Product Line Engineering Research Articles

Model‐Based Product Line Engineering to Plan and Track Submarine Configuration

ABSTRACTOne can measure the life of a class of submarines in decades. As such, the operational demands and expectations change both strategically and tactically over its lifetime. Coupling this adaptability with the length of time submarines take to design, build, and maintain, no two as‐built submarines in a class will ever be the same, even when constructed/maintained to the same build‐to design. Traditionally, we have accepted this as the standard, and in most cases the configuration management team managed the full information set at a class or batch level, but not at an individual submarine level. The team managed the configuration of an individual submarine in terms of agreed changes against the class or batch baseline. Advances in technology (hardware performance, software tools, and standards) now give us the opportunity to not only manage the full information set related to individual submarine system configuration baselines as they change over time, but also undertake rigorous model‐based trade‐off studies to plan the manner in which we can modify a class, a batch, an individual submarine (variant), or any combination thereof over time. This paper will explore the use of model‐based systems engineering (MBSE) coupled with recent developments of product line engineering (PLE)/orthogonal variability modelling (OVM) to provide a means to plan, track, manage, and evaluate an individual submarine's configuration over time in the context of the class, whilst simultaneously highlighting the wider application in the submarine enterprise and beyond.

Where the Big Bucks Will Come From: Implementing Product Line Engineering for Railway Rolling Stock

ABSTRACTThis paper presents a mid‐term return of experience from Alstom's rolling stock division on its implementation of product line engineering (PLE). It describes the journey we have undertaken to implement PLE, the challenges we have had to face, and the manner in which we are finding our way (most of the time deliberately, sometimes fortuitously) to define a profitable reuse strategy and to structure our approach on PLE. The paper also presents some encouraging intermediate results and concludes with the perspectives and the future challenges in our search for product line systems engineering excellence.

Variability management in safety‐critical systems design and dependability analysis

AbstractSafety‐critical systems are of paramount importance for many application domains, where safety properties are a key driver to engineer critical aspects and avoid system failures. For the benefits of large‐scale reuse, software product lines (SPL) have been adopted in critical systems industry. However, the integration of safety analysis in the SPL development process is nontrivial. Also, the different usage contexts of safety‐critical systems complicates component fault modeling tasks and the identification of potential hazards. In this light, better methods become necessary to estimate the impact of dependability properties during Hazard Analysis and Risk Assessment. Existing methods incorporating the analysis of safety properties in SPL are limited as they do not include hazard analysis and component fault modeling. In this paper, we present the novel DEPendable Software Product Line Engineering (DEPendable‐SPLE) approach, which extends traditional SPL processes to support the reuse of safety assets. We also present a detailed analysis of the impact of product and context features on the SPL design, safety analysis, and safety requirements. We applied DEPendable‐SPLE to a realistic case study from the aerospace domain to illustrate how to model and reuse safety properties. DEPendable‐SPLE reduced the effort of safety analysis for certifying system variants.

The new era of software reuse

The new era of software reuse

A goal-driven software product line approach for evolving multi-agent systems in the Internet of Things

Multi-agent systems have proved to be a suitable technology for developing self-adaptive Internet of Things (IoT) systems, able to make the most appropriate decisions to address unexpected situations. This leads to new opportunities to use multi-agent technologies to develop all kinds of cyber–physical systems, which usually encompass a high diversity of devices (e.g., new home appliances). The heterogeneity of devices and the high diversity of the available technology, demand the explicit modeling of all kinds of variability for ultra-large systems. However, multi-agent systems lack mechanisms to effectively deal with the different degrees of variability present in these kinds of systems. Software Product Line (SPL) technologies, including variability models, have been successfully applied to different domains to explicitly model variability in hardware, system requirements or user-intended goals. In addition, current market trends are unpredictable, imposing novel technologies, new requirements and goals that must be incorporated immediately into the running systems without damaging them. In this paper, we combine goal-driven and SPL approaches to develop and drive the evolution of multi-agent systems in the context of cyber–physical systems. We propose an SPL process and an evolution process that define a set of models (iStar 2.0 for goals and CVL models for variability) and algorithms to automatically propagate changes to agents running in multiple heterogeneous devices, each of them with a different configuration. We illustrate the proposal in the context of a home energy management system. Finally, we have tested the scalability and performance of the proposal using randomly generated models. The results show that with our approach it is possible to manage huge iStar models of 10000 elements in seconds.

FCA for software product line representation: Mixing configuration and feature relationships in a unique canonical representation

Software Product Line Engineering (SPLE) is a set of methods to help build a collection of software systems which are similar enough to enable appropriate artefact reuse. An important task consists in documenting in variability models the common and variable features which may compose the similar software systems along with compatibility constraints between these features. Several models and formalisms have been proposed to model variability: each one of them has specific properties making it pertinent to support certain management operations, which are of primary importance in SPLE. Switching from one kind of variability model to another is thus important to benefit from a wide range of operations and efficiently manage a software product line. In this paper, we review the various approaches proposed to manage and organise features and product configurations (a product configuration being a chosen subset of features). We discuss the originality of concept lattices, canonical structures presenting a dual view on features and configurations, and the advantages to use these structures as variability representations. Switching from existing variability models to an equivalent concept lattice raises scaling issues related to the size of the needed input dataset and thus hinders their exploitation. We propose an alternative relying on implicative systems and define a transformation method which does not suffer from scaling issues.

Applying Feature‐Based Systems and Software Product Line Engineering in Unclassified and Classified Environments

AbstractGlobal aerospace and defense companies are reaping the benefits of feature‐based systems and software product line engineering and management (FBPLE) in those situations where production must seamlessly span unclassified and classified environments (Gregg et al. 2014) (Gregg et al. 2015) (Krueger et al. 2014) (Lanman et al. 2011). These benefits include leveraging company talent while awaiting access to classified material; leveraging employees who are members of other sovereign states; and optimizing system production and maintenance for export / import. In this whitepaper we present the architectural design and accompanying business processes for a PLE factory and its artifacts that comprise unclassified and classified digital assets1. These digital assets are used in automated generation of unclassified and classified product instances. All production activities occur within a single logical enterprise spanning multiple information systems comprising multiple security zones2.

Towards complex product line variability modelling: Mining relationships from non-boolean descriptions

Software product line engineering relies on systematic reuse and mass customisation to reduce the development time and cost of a software system family. The extractive adoption of a product line requires to extract variability information from the description of a collection of existing software systems to model their variability. With the increasing complexity of software systems, software product line engineering faces new challenges including variability extraction and modelling. Extensions of existing boolean variability models, such as multi-valued attributes or UML-like cardinalities, were proposed to enhance their expressiveness and support variability modelling in complex product lines. In this paper, we propose an approach to extract complex variability information, i.e., involving features as well as multi-valued attributes and cardinalities, in the form of logical relationships. This approach is based on Formal Concept Analysis and Pattern Structures, two mathematical frameworks for knowledge discovery that bring theoretical foundations to complex variability extraction algorithms. We present an application on product comparison matrices representing complex descriptions of software system families. We show that our method does not suffer from scalability issues and extracts all pertinent relationships, but that it also extracts numerous accidental relationships that need to be filtered.

Transformation towards agile software product line engineering in large companies: A literature review

AbstractAlmost all companies struggle with software systems that are getting increasingly complex. Therefore, in particular large companies often use software product lines, which provide an efficient way to manage software reuse as well as the high complexity. However, software product lines seem to be too slow to react to changes. Agile development approaches promise to meet this demand. At the same time, integrating an agile approach is not always easy. Consequently, there is a need for a transformation model that supports the agile transformation without harming the benefits of software product lines.In this paper, we present the results of a literature review regarding agile transformation in large software companies. Our results summarize the insights from 85 papers and present a list of 20 tasks and tasks that are recommended by several researchers to be considered during the transformation. On the basis of these results, we create an agile transformation model—the so‐called “agile hamburger”—that contains these tasks and preserves software product lines. Since the model is rather coarse grained and generic, we also support the adaption to different teams by presenting an assessment model tailored to the demands of the automotive domain.

FM-CF: A framework for classifying feature model building approaches

Software product line engineering has emerged as a prominent software engineering paradigm, as it comprises a set of core assets sharing functionality and quality attributes. Feature modelling is one of the most frequently used techniques for modelling the variability within a software product line. There are several proposals for building Feature Models which rely on semi-automated or fully automated means. Unfortunately, automatic feature model construction has been addressed from different viewpoints, so it is not easy to know which is the best approach for automating the building of variability models. In fact, there is no clarity regarding common elements, and the main differences that characterise such approaches. Additionally, the wide variety of terms used to refer to the process of building a Feature Model (e.g. synthesis, location, re-engineering, and weaving) means that approaches are varied and very heterogeneous, making them complex to understand and classify. This paper introduces FM-CF, which is a Conceptual experience-based Framework for classifying approaches for the automatic building of Feature Models. The framework considers a set of categories mainly focused on characterising some aspects, such as input sources, methods and techniques, results, and types of evaluation. A literature review of (semi-) automated Feature Model construction was performed to identify approaches for building Feature Models by (semi-)automatic means, and the main terms used by those approaches. Then the completeness of the framework was evaluated by mapping the set of dimensions and their items, and the terms extracted from the literature.The conceptual framework provides guidance to researchers for choosing the appropriate aspects with which to build Feature Models, and helps in the understanding and clarification of the proposed approaches.

Leveraging product line engineering for the development of domain-specific metamodeling languages

Abstract A domain-specific metamodeling language (DSM2L) enables language engineers to define a family of similar metamodel-based languages. In recent years, several DSM2Ls have been developed for various domains, e.g., traceability, variability management, process modeling, and metamodeling feature models. However, there is no consensus on an engineering approach for constructing a DSM2L. To address this problem, we consider a DSM2L as a software product line (SPL), in which the software products are the family of languages. Based on this assumption, we propose a roadmap to develop a DSM2L using an SPL engineering framework. To investigate the pros and cons of the roadmap, the MoDEBiTE metamodeling language is engineered in the domain of bidirectional transformations. In order to validate the proposal of engineering a DSM2L, an experiment with six transformation cases is performed on MoDEBiTE. The results of the experiment show the applicability, usefulness, and validity of MoDEBiTE, demonstrating the validity of the proposal.

Modal transition system encoding of featured transition systems

Featured transition systems (FTSs) and modal transition systems (MTSs) are two of the most prominent and well-studied formalisms for modeling and analyzing behavioral variability as apparent in software product line engineering. On one hand, it is well-known that for finite behavior FTSs are strictly more expressive than MTSs, essentially due to the inability of MTSs to express logically constrained behavioral variability such as persistently exclusive behaviors. On the other hand, MTSs enjoy many desirable formal properties such as compositionality of semantic refinement and parallel composition. In order to finally consolidate the two formalisms for variability modeling, we establish a rigorous connection between FTSs and MTSs by means of an encoding of one FTS into an equivalent set of multiple MTSs. To this end, we split the structure of an FTS into several MTSs whenever it is necessary to denote exclusive choices that are not expressible in a single MTS. Moreover, extra care is taken when dealing with infinite behavior: loops may have to be unrolled to accumulate FTS path constraints when encoding them into MTSs. We prove our encoding to be semantic-preserving (i.e., the resulting set of MTSs induces, up to bisimulation, the same set of derivable variants as their FTS counterpart) and to commute with modal refinement. We further give an algorithm to calculate a concise representation of a given FTS as a minimal set of MTSs. Finally, we present experimental results gained from applying a tool implementation of our approach to a collection of case studies.

Modelling equivalence classes of feature models with concept lattices to assist their extraction from product descriptions

Software product line engineering gathers a set of methods to help create, manage and maintain a collection of similar software systems. Variability modelling is a focal point of this paradigm, where feature models (FMs) are the prevalent notation. Migration from single system development to software product lines is a spreading topic in software engineering. To ease the migration, research has been done to automatically extract FMs from software descriptions, but most of these approaches are defined in a functional manner based on an ad-hoc variability analysis. In this paper, we propose a theoretical view on FM extraction from software descriptions based on Formal Concept Analysis (FCA). It is a structural framework for variability representation which allows to lay down theoretical foundation to variability extraction. We propose an original mapping between relationships expressed in FMs and the ones emphasised in FCA conceptual structures. We show that conceptual structures represent equivalence classes of FMs that steer the user choices during their synthesis, and propose a reverse engineering method based on them. We discuss its applicability and show that the combinatorial explosion of concept lattices can be avoided by the use of two sub-orders embodying the necessary information concerning variability.

Integrated revision and variation control for evolving model-driven software product lines

Software engineering projects are faced with abstraction, which is achieved by software models, historical evolution, which is addressed by revision control, and variability, which is managed with the help of software product line engineering. Addressing these phenomena by separate tools ignores obvious overlaps and therefore fails at exploiting synergies between revision and variation control for models. In this article, we present a conceptual framework for integrated revision and variation control of model-driven software projects. The framework reuses the abstractions of revision graphs and feature models and follows an iterative, revision-control-like approach to software product line engineering called product-based product line development. A single version (i.e., a variant of a selected revision) is made available in a workspace, where the user may apply arbitrary modifications. Based on a user-provided specification of the affected variants, the changes are automatically written back to a transparent repository that relies on an internal multi-version storage. The uniform handling of revisions and variants of models is achieved by transparently mapping version concepts to a semantic base layer, which is defined upon propositional logic. At the heart of the conceptual framework is a dynamic filtered editing model, which allows that the versioned artifacts and the feature model co-evolve. We contribute algorithms for checkout and commit, which satisfy a set of consistency constraints referring to variant specifications in an evolving feature model. This article furthermore addresses the orchestration of collaborative development by distributed replication and the well formedness of text and model artifacts to be checked out into the workspace. The Eclipse-based tool SuperMod demonstrates the feasibility of the conceptual framework. It allows the user to reuse arbitrary editing tools for text-based programming and/or Ecore-based modeling languages. An evaluation based on three case studies investigates the properties of SuperMod with a specific focus on filtered editing. The evaluation demonstrates that the dynamic filtered editing model reduces the cognitive complexity and the amount of user interaction necessary for variation control when compared to unfiltered model-driven approaches to software product line engineering.

Achieving change requirements of feature models by an evolutionary approach

Feature models are a widely used modeling notation for variability and commonality management in software product line (SPL) engineering. In order to keep an SPL and its feature model aligned, feature models must be changed by including/excluding new features and products, either because faults in the model are found or to reflect the normal evolution of the SPL. The modification of the feature model to be made to satisfy these change requirements can be complex and error-prone. In this paper, we present a method that is able to automatically update a feature model in order to satisfy a given update request. The method is based on an evolutionary algorithm that iteratively applies structure-preserving mutations to the original model, until the model is completely updated or some other termination condition occurs. Among all the possible models achieving the update request, the method privileges those structurally simpler. We evaluate the approach on real-world feature models; although it does not guarantee to completely update all the possible feature models, empirical analysis shows that, on average, around 89% of requested changes are applied.

Product Line Configuration Meets Process Mining

Product line engineering is a new production paradigm that provides organizations a competitive edge by improving productivity and decreasing costs. The purpose with this new production paradigm is no longer to develop a single product but to develop a product family and to generate the products of the line through configuration processes. However, the potential benefits of product line engineering can be missed when dealing with large product lines because the configuration processes become error prone tasks. Consequently, guiding stakeholders during such complex configuration processes and recommending the best configuration alternatives until leading to a satisfying experience becomes a challenge. This paper focuses on enhancing product line configuration processes through process mining techniques. Therefore, user’s actions of previous product line configurations are logged, mined and analyzed. We conducted a preliminary research to motivate the advantages of process mining in product line configuration and to explore what can process mining bring for configuration processes and how to use it to enhance configuration processes. Thus, guidance questions are sketched in order to position process mining as a solving tool for the configuration difficulties. Furthermore, we propose a reference architecture that considers process mining for configuring product lines.

Towards Software Product Lines Optimization Using Evolutionary Algorithms

Software product line (SPL) engineering methodology assist to create a range of software products within less time and cost but with high quality by the reuse of core software assets, which has been tested. Thus, testing is crucial for successfully deploying SPL. As the product features increases, testing process can be time-consuming. Testing in SPL is regarded as a combinatorial optimization problem. Evolutionary algorithms were reported to provide good results in such class of problems.This research provides a framework to compare different multi-objective Evolutionary Algorithms performance regarding software product line context. We report on the problem encoding, variation operators and different types of algorithms: Indicator Based Evolutionary Algorithm (IBEA), Strength Pareto Evolutionary algorithm II (SPEA-II), Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The framework will provide preliminary results on different Feature Models (FMs) to measure their feasibility to optimize SPL testing.

Modeling Requirements of Multiple Single Products to Feature Model

This work investigates how the requirements of multiple single products can be modeled into a feature model as part of domain engineering process in software product line engineering (SPLE) methodology. It adopts an extractive strategy in devising a two-step process for creating the feature model. The first step is by identifying the business process and list of features across all products into a product roadmap. The second step is identifying the commonality and variability of features based on the product roadmap into a feature model. The proposed approach may help a software company that produces software products for certain domain to migrate from traditional single product development lifecycle to software product line development lifecycle.

Glencoe – a tool and a methodology to manage variability within the product development process

Manufacturers of all business areas are faced with growing customer demands and the corresponding complexity of their products. The need to offer highly customised variants becomes obvious especially in the automotive industry. New car models and respective variants with configurable features are offered to customers in increasingly shorter intervals. However, the production costs are expected to remain stable. A known industry approved approach to systematically design customisable products at reasonable costs are multivariant product lines: product line engineers compose models describing common and distinctive product features as well as their dependencies. Subsequently these models constitute the basis for formal analyses and optimisations of the corresponding product lines. In this paper we address typical problems in this area and how the presented methodology handles these problems. The free to use web application Glencoe implements main parts of this methodology. It is shown how Glencoe can be employed in industrial practice.

Extracting core requirements for software product lines

Software Product Line Engineering (SPLE) is a promising paradigm for reusing knowledge and artifacts among similar software products. However, SPLE methods and techniques require a high up-front investment and hence are profitable if several similar software products are developed. Thus in practice adoption of SPLE commonly takes a bottom-up approach, in which analyzing the commonality and variability of existing products and transforming them into reusable ones (termed core assets) are needed. These time-consuming and error-prone tasks call for automation. The literature partially deals with solutions for early software development stages, mainly in the form of variability analysis. We aim for further creation of core requirements—reusable requirements that can be adapted for different software products. To this end, we introduce an automated extractive method, named CoreReq, to generate core requirements from product requirements written in a natural language. The approach clusters similar requirements, captures variable parts utilizing natural language processing techniques, and generates core requirements following an ontological variability framework. Focusing on cloning scenarios, we evaluated CoreReq through examples and a controlled experiment. Based on the results, we claim that core requirements generation with CoreReq is feasible and usable for specifying requirements of new similar products in cloning scenarios.