To connect or not to connect

In the software product line research, product variants typically differ by their functionality and quality attributes are not purposefully varied. The goal is to study purposeful performance variability in software product lines, in particular, the motivation to vary performance, and the strategy for realizing performance variability in the product line architecture. The research method was a theory-building case study that was augmented with a systematic literature review. The case was a mobile network base station product line with capacity variability. The data collection, analysis and theorizing were conducted in several stages: the initial case study results were augmented with accounts from the literature. We constructed three theoretical models to explain and characterize performance variability in software product lines: the models aim to be generalizable beyond the single case. The results describe capacity variability in a base station product line. Thereafter, theoretical models of performance variability in software product lines in general are proposed. Performance variability is motivated by customer needs and characteristics, by trade-offs and by varying operating environment constraints. Performance variability can be realized by hardware or software means; moreover, the software can either realize performance differences in an emergent way through impacts from other variability or by utilizing purposeful varying design tactics. The results point out two differences compared with the prevailing literature. Firstly, when the customer needs and characteristics enable price differentiation, performance may be varied even with no trade-offs or production cost differences involved. Secondly, due to the dominance of feature modeling, the literature focuses on the impact management realization. However, performance variability can be realized through purposeful design tactics to downgrade the available software resources and by having more efficient hardware.

Software engineering in the automotive domain faces some outstanding challenges in terms of variability and complexity, specific customer relationship with car manufacturers and quickly and continuously changing requirements. Especially power train control systems currently evolve from classic combustions engine focused concepts towards highly divergent solutions like hybrid systems, range extenders or fully electrically driven vehicles. In this context Bosch's business units Diesel Systems (DS) and Gasoline systems (GS) merged their separated engine control unit development organizations, motivated by a growing portion of functionality independent of the combustion engine type and an increasing synergy potential in hardware and software development. As DS and GS software development evolved independently, they established different technical solutions, organizational structures and development processes - even though both adopted product line engineering principles. Since the official start in 2008, DGS-EC (Diesel Gasoline Systems Engine Control) has installed important prerequisites for common, unified software development and achieved many unification targets. This paper gives an insight to the most important experiences of this large scale product line merger, addressing challenges, enablers and solutions concerning organizational setup, process unification, architecture and product line scope definition as well as software technology harmonization.

This article presents a strategy that aims to support model-oriented software product line architects on making the decisions required to derive products. Together with the strategy, we present a tool that uses the power of constraint programming to create and use decision models. The models make it possible to relate model transformation rules and product line variants (alternatives) in order to enable the selection of the necessary rules according to the variants selected by a product designer. The greatest innovation of our contribution is that we provide architects with a tool that takes into account the possible interactions between variants of a product line and makes it possible to model the decisions required to derive products reusing existing model transformation rules as much as possible.

In this chapter, three bio-inspired collaborative controls are proposed for multi-level systems. Firstly, a novel bio-inspired multi-layered intelligent cooperative controller (BMLICC) is proposed. Secondly, a novel cytokine-network-based stretching network (CNSN) is proposed to achieve the desired control performance in large production lines. Finally, a novel immune-based self–adaptive collaborative control allocation (ICCA) method is proposed for multi-level stretching processes in large industrial production lines.

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.

Product line software engineering empowers high-level constructive software reuse by exploiting commonality and managing variability among the member products in a product family. A new feature-oriented method has been proposed to model feature variability and dependencies in software product lines. To conquer the complexity of the modelling, the modelling is decomposed into two views - a feature tree view and a dependency view. The main focus of this work is on the dependency view. Feature dependencies have very strong implications on the configurations of member products in a product line. Currently existing feature-oriented approaches do not facilitate feature dependency modelling in a scalable way. A matrix-based approach is developed to maintain the information about feature dependencies, and to accommodate the generation of feature dependency diagrams. Instead of using an overall feature dependency diagram, the authors decompose an overall diagram into a set of individual feature dependency diagrams that is capable of facilitating more effective configurations for the member products in a product line. The approach is scalable for constantly evolved product lines and supports easy detection of conflicting feature dependencies.

Variability-rich Software Ecosystems need configuration capabilities just as in any product line. However, various additional capabilities are required, taking into account the software ecosystem characteristics. In order to address these specific needs, we developed the Integrated Variability Modeling Language (IVML) for describing configurations of variability-rich software ecosystems. IVML is a variability modeling and configuration language along with accompanying reasoning facilities.

A product line definition must cover several systems, for this reason additional requirements are included as product line assets during domain engineering. Generic assets are presented to cover all components the product line instances are built from, and their corresponding composition rules. These generic assets embrace common and variable product aspects supporting the variability in product line definition and instantiation.This paper is devoted to present the problem of handling product line variability in every life-cycle stage by the integration of the ideas of the domain engineering method FORM (Feature-Oriented Reuse Method) and the Mecano Model, which defines a coarse-grained reusable element structure.

Reengineering a legacy product line has been addressed very little by current product line research activities. This paper introduces a method to investigate feature dependencies and interactions, which restricts the variants that can be derived from the legacy product line assets. Reorganizing the product line assets with respect to new requirements requires more knowledge than what is easily provided by the classical feature-modeling approaches. Hence, adding all the feature dependencies and interactions into the feature tree results in unreadable and unmanageable feature models that fail to achieve their original goals.We therefore propose two complementary views to represent the feature model. One view shows the hierarchical refinement of features similar to common feature-modeling approaches in a feature tree. The second view describes what kind of dependencies and interactions there are between various features.We show two examples of feature dependencies and interactions in the context of an engine-control software product line, and we demonstrate how our approach helps to define correct product configurations from product line variants.

This paper proposes a methodology for the evaluation of the performance of multistage multiproduct production lines, based on discrete-event simulation models, and developed in an Arena simulation environment. Its purpose is that of analysing blockage and starvation phenomena on a complex production line so as to assist in the decision-making process, since it allows for a greater understanding of the functioning of the production line. This is undertaken through the variation of different parameters, thus contributing to a better specification, characterization and definition of the most efficient planning and control system to be implemented in multistage multiproduct manufacturing processes. The methodology was validated by performing different simulation experiments and will be applied to a multistage multiproduct production line, based on a real case, with a closed-loop network configuration of machines and intermediate buffers consisting of conveyors, which is very common in the automobile sector. There are, in fact, very few studies undertaken which use simulation as a support tool in the decision-making process in the context of networks of closed-loop production lines.

This paper studies the problem of allocating buffers in stochastic production flow lines with product travel time. We build a model that decomposes the production line into the S-B-S (Station-Buffer-Station) subsystems, and use queueing theory to aggregate the subsystems. Experiments are designed for both balanced and unbalanced production lines, and with the computational results, some general rules for the buffer allocation problem are proposed. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4520 Full Text: PDF

Today product manufacturers are concerned with an ever growing complexity of their respective products. The main complexity driver has been identified as the variability of products. It results from the constantly growing need to individualize products according to customer needs or market constraints. Car manufacturers are typical examples for such companies. Talking about ten to the power of 8 variants of one car, it seems nearly impossible to overlook the corresponding product line and the consequences of changes to the product line. In literature there are several approaches to visualize the variability of such product lines. But according to our review of the corresponding literature we made the experience that there is no one-fits-it-all visualization technique. The Glencoe project, hosted at the Trier University of Applied Sciences, aims at providing a rapid visualization prototyping framework giving the possibility to quickly implement the preferred visualization technique and test it during a proof of concept under industrial conditions. The chosen framework platform allows not only to run the programmed visualization on desktop machines, but also on tablets. In this paper we present a student project realizing different views of features trees as well as views for logical constraints based on the Glencoe platform.

Over the past few decades, buffer allocation problems have been investigated extensively. It is widely known that buffers play important roles in the manufacturing system, including preparing disruption and improving efficiency. However, due to a physical limit for buffer space and the related cost burden, optimal buffer allocation has become an important issue. In this paper, a solution for optimal buffer allocation in a flexible manufacturing system (FMS) with finite buffers is presented. Unlike most previous studies, which mainly focused on input buffers only and operation dispatching in simple production lines, this paper considers both input and output buffers together in complex production lines where multiple workstations, multiple automatic guided vehicles, and complicated paths exist. Also, we address a practical buffer allocation problem by increasing total number of buffers and changing a buffer profile (capacities of multiple input/output buffers). A simulation and a genetic algorithm are used to solve our problem. While the simulation models the process of the complex FMS, the genetic algorithm determines the optimal buffer allocation. The proposed solution performs better than the conventional heuristic used in practice.

We describe a simulated annealing approach for solving the buffer allocation problem in reliable production lines. The problem entails the determination of near optimal buffer allocation plans in large production lines with the objective of maximizing their average throughput. The latter is calculated utilizing a decomposition method. The allocation plan is calculated subject to a given amount of total buffer slots in a computationally efficient way.

A heuristic algorithm for the buffer allocation in unreliable unbalanced production lines