Optimal Modeling Language and Framework for Schedulable Systems

Context. From recent works on product properties resulting from configurations and the optimisation of these properties, one comes quickly to more complex challenges such as multi-objective optimisation, conflicting objectives, multiple stakeholders, and conflict resolution. The intuition is that Software Product Line Engineering (SPLE) can draw from other disciplines that deal with decision spaces and complex decision scenarios.Objectives. We aim to (1) explore links to such disciplines, (2) systematise and compare concepts, and (3) identify opportunities, where SPLE approaches can be enriched.Method. We undertake an exploratory study: Starting from common SPLE activities and artefacts, we identify aspects where we expect to find corresponding counterparts in other disciplines. We focus on Multiple Criteria Decision Analysis (MCDA), Multi-Objective Optimisation (MOO), and Design Space Exploration (DSE), and perform a comparison of the key concepts.Results. The resulting comparison relates SPLE activities and artefacts to concepts from MCDA, MOO, and DSE and identifies areas where SPLE approaches can be enriched. We also provide examples of existing work at the intersections of SPLE with the other fields. These findings are aimed to foster the conversation on research opportunities where SPLE can draw techniques from other disciplines dealing with complex decision scenarios.

Software Product Line Engineering (SPLE) is an approach for software reuse. It concerns to produce customized software products as atomic or composite services to be reused in SOA-based applications. A common set of artifacts is used to build these services in a planned and managed way. The main purpose of SPLE is to explore commonalities and variabilities. The SPLE approach provides a strategic software reuse that can produce quality Software as a Service (SaaS) while cutting cost and reducing time-to-market. This paper proposes a process to construct services as Software Product Lines by using Model Driven techniques. The process combines the use of maps, visual techniques for SPL modeling, especially features diagrams and MD techniques. In addition to the process, we have developed a tool to support map, feature, and class diagrams modeling.

Cloned code clustering identifies identical or similar code fragments, i.e., cloned code, from products of a product family, and then constructs cloned code clusters, a set of files, each of which contains the same cloned code. It is an essential step for migrating from the Clone-And-Own (CAO) approach to the Software Product Line Engineering approach for developing a family of products. This paper proposes a method for cloned code clustering based on files that share cloned code. The method proposed in this paper identifies clusters to use for constructing a product line platform at the source code level in such a way that it works regardless of cloning-in-the-small or cloning-in-the-large; it does not need to know what code is original or what code is cloned; and its results remain consistent regardless of the comparison order or the relative similarity of products of a product family. For evaluation we applied our method to Apo-Games and ArgoUML developed with the CAO approach and confirmed that our method correctly constructs code clusters.

While the use of cloud computing is on the rise, many obstacles to its adoption remain. One of the weaknesses of current cloud offerings is the difficulty of developing highly customizable applications while retaining the increased scalability and lower cost offered by the multi-tenant nature of cloud applications. In this paper we describe a Software Product Line Engineering (SPLE) approach to the modelling and deployment of customizable Software as a Service (SaaS) applications. Afterwards we define a formal feature placement problem to manage these applications, and compare several heuristic approaches to solve the problem. The scalability and performance of the algorithms is investigated in detail. Our experiments show that the heuristics scale and perform well for systems with a reasonable load.

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.

Software Product Line Engineering (SPLE) has been proven to reduce development and maintenance costs, improving the time-to-market, and increasing the quality of product variants developed from a product family via systematic reuse of its core assets. SPLE has been successfully used in the development of safety-critical systems, especially in automotive and aerospace domains. Safety-critical systems have to be developed according to safety standards, which demands safety analysis, Fault Tree Analysis (FTA), and assurance cases safety engineering artefacts. However, performing safety analysis, FTA, and assurance case construction activities from scratch and manually for each product variant is time-consuming and error-prone, whereas variability in safety engineering artefacts can be automatically managed with the support of variant management techniques. As safety is context-dependent, context and design variation directly impact in the safety properties changing hazards, their causes, the risks posed by these hazards to system safety, risk mitigation measures, and FTA results. Therefore, managing variability in safety artefacts from different levels of abstraction increases the complexity of the variability model, even with the support of variant management techniques. To achieve an effective balance between benefits and complexity in adopting an SPLE approach for safety-critical systems it is necessary to distinguish between reusable safety artefacts, whose variability should be managed, and those that should be generated from the reused safety artefacts. On the other hand, both industry and safety standards have recognized the use of model-based techniques to support safety analysis and assurance cases. Compositional safety analysis, design optimization, and model-based assurance cases are examples of techniques that have been used to support the generation of safety artefacts required to achieve safety certification. This thesis aims to propose a model-based approach that integrates modelbased development, compositional safety analysis, and variant management techniques to support the systematic reuse and generation of safety artefacts in safety-critical software product line engineering. The approach contributes to reduce the effort and costs of performing safety analysis and assessment for a particular product variant, since such analysis is performed from the reused safety artefacts. Thus, variant-specific fault trees, Failure Modes and Effects Analysis (FMEA), and assurance case artefacts required to achieve safety certification can be automatically generated with the support the model-based safety analysis and assurance case construction techniques.

Software Product Line Engineering (SPLE) is a software development paradigm that aims at reducing the development effort and shorting time-to-market through systematic software reuse. While this paradigm has been successfully applied for the development of embedded systems in various domains, new challenges have emerged from the development of safety critical systems that require certification against a specific standard. Existing SPLE approaches do not explicitly consider the various certification standards or levels that products should satisfy. In this paper, we focus on several practical issues involved in the SPLE process, establishing an infrastructure of a product line engineering for certified products. A metamodel is proposed to capture the entities involved in SPL certification and the relationships among them. ProLiCES, which is a model-driven process for the development of SPLs, was modified to serve as an example of our approach, in the context of the UAV (Unmanned Aerial Vehicle) domain.

Product line software engineering (PLSE) is an emerging software engineering paradigm, which guides organizations toward the development of products from core assets rather than the development of products one by one from scratch. In order to develop highly reusable core assets, PLSE must have the ability to exploit commonality and manage variability among products from a domain perspective. Feature modeling is one of the most popular domain analysis techniques, which analyzes commonality and variability in a domain to develop highly reusable core assets for a product line. Various attempts have been made to extend and apply it to the development of software product lines. However, feature modeling can be difficult and time-consuming without a precise understanding of the goals of feature modeling and the aid of practical guidelines. In this paper, we clarify the concept of features and the goals of feature modeling, and provide practical guidelines for successful product line software engineering. The authors have extensively used feature modeling in several industrial product line projects and the guidelines described in this paper are based on these experiences.

This research was driven by the need for a more efficient production scheduling system in a consumable liquid product division of a large consumer products company. The manufacturing process under inspection consists of continuous and discrete elements, on both production and packaging lines. The production lines are split into continuous production lines and batch production lines which produce the product in fixed batch amounts. Then there are several bottling lines, some of which package a particular bottle size and others that can package multiple bottle sizes. The main objective of this research was to reduce the amount of time it takes for the client to create production and bottling schedules. An optimization model was developed to automate this process and provide the client with the best possible schedule. The objective of the model is to minimize cost by minimizing the number of switches across the production and bottling lines, as well as minimizing the amount of overproduction. Inputs into the model include model parameters, like the number of shifts to schedule, and monthly demand numbers for each stock keeping unit (SKU). The variables being solved for are the amount of each flavor to be produced across the production lines during each shift, and the number of bottles of each SKU to be bottled across the bottling lines during each shift. Due to the unique constraints and resources of the client, a custom formulation using mixed integer programming was necessary to achieve these objectives. Overall, our model fell short in some areas but succeeded in others. Our analysis showed that the model had a 13% average decrease in production switches but an 87% average increase in bottling switches compared to the current manual scheduling system. However, the ability of our system to create a good enough initial schedule reduces the time it takes expert human schedulers to develop a final schedule by up to 85%. Runtime and computational constraints barred us from creating an optimal, cost-minimized solution for our client, and future work can be directed toward solving these issues.

In software product line engineering (SPLE), commonality and variability across products of a product line domain are captured typically by a feature model. Reusable components are then developed from features. However, mapping features to components remains a complex task requiring a systematic way of exploring and analyzing various concerns arising from inadequate/insufficient domain assumptions. Essentially, those concerns prevent SPLE from achieving various quality attributes. This paper proposes a problem frames-based approach to addressing this problem. An elevator product line example is used to demonstrate the feasibility of the approach.

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

Version control (VC) and Software Product Line Engineering (SPLE) are two software engineering disciplines to manage variability in time and variability in space. In this paper, a thorough comparison of VC and SPLE is provided, showing that both disciplines imply a number of desirable properties. As a proof of concept for the combination of VC and SPLE, we present SuperMod, a tool realizes an existing conceptual framework that transfers the iterative VC editing model to SPLE. The tool allows to develop a software product line in a single-version workspace step by step, while variability management is completely automated. It offers familiar version control metaphors such as check-out and commit, and in addition uses the SPLE concepts of feature models and feature configuration the definition of logical variability and to define the logical scope of a change. SuperMod has been implemented in a model-driven way and primarily targets EMF models as software artifacts. We successfully apply the tool to a standard SPLE example.

Software product line engineering (SPLE) enables systematic reuse in development of a family of related software systems by explicitly defining commonalities and variabilities among the individual products in the family. Nowadays, SPLE is used in a variety of complex domains such as avionics and automotive. As such domains include safety critical systems which exhibit probabilistic behavior, there is a major need for modeling and verification approaches dealing with probabilistic aspects of systems in the presence of variabilities. In this paper, we introduce a mathematical model, Discrete Time Markov Chain Family (DTMCF), which compactly represents the probabilistic behavior of all the products in the product line. We also provide a probabilistic model checking method to verify DTMCFs against Probabilistic Computation Tree Logic (PCTL) properties. This way, instead of verifying each product individually, the whole family is model checked at once, resulting in the set of products satisfying the desired property. This reduces the required cost for model checking by eliminating redundant processing caused by the commonalities among the products.

Software architecture has been a key research area in the software engineering community due to its significant role in creating high-quality software. The trend of developing product lines rather than single products has made the software product line a viable option in the industry. Software product line architecture (SPLA) is regarded as one of the crucial components in the product lines, since all of the resulting products share this common architecture. The increased popularity of software product lines demands a process maturity evaluation methodology. Consequently, this paper presents an architecture process maturity model for software product line engineering to evaluate the current maturity of the product line architecture development process in an organization. Assessment questionnaires and a rating methodology comprise the framework of this model. The objective of the questionnaires is to collect information about the SPLA development process. Thus, in general this work contributes towards the establishment of a comprehensive and unified strategy for the process maturity evaluation of software product line engineering. Furthermore, we conducted two case studies and reported the assessment results, which show the maturity of the architecture development process in two organizations.

A software product line (SPL) usually involves a shared set of core assets and a series of application products. To ensure consistency, the evolution of the core assets and all the application products should be coordinated and synchronized under a unified evolution process. Therefore, SPL evolution often involves cross-product propagation and synchronization besides application derivation based on core assets, presenting quite different characteristic from the evolution of individual software products. As software architectures, including the product line architecture (PLA) and application architectures, play a central role in SPL engineering and evolution, architecture-based evolution analysis is a natural way for analyzing and managing SPL evolution. In this paper, we explore common practices of architecture evolution and the rationale behind in industrial SPL development. To this end, we conduct a case study with Wingsoft examination system product line (WES-PL), an industrial product line with an evolution history of eight years and more than 10 application products. In the case study, we reviewed the evolution history of WES-PL architecture and analyzed several typical evolution cases. Based on the historical analysis, we identify some special problems in industrial SPL practice from the aspect of architecture evolution and summarize some useful experiences about SPL evolution decisions to complement classical SPL methodology. On the other hand, we also propose some possible improvements for the evolution management in WES-PL.