An industrial case study on variability handling in large enterprise software systems

ContextEnterprise software systems (e.g., enterprise resource planning software) are often deployed in different contexts (e.g., different organizations or different business units or branches of one organization). However, even though organizations, business units or branches have the same or similar business goals, they may differ in how they achieve these goals. Thus, many enterprise software systems are subject to variability and adapted depending on the context in which they are used.ObjectiveOur goal is to provide a snapshot of variability in large scale enterprise software systems. We aim at understanding the types of variability that occur in large industrial enterprise software systems. Furthermore, we aim at identifying how variability is handled in such systems.MethodWe performed an exploratory case study in two large software organizations, involving two large enterprise software systems. Data were collected through interviews and document analysis. Data were analyzed following a grounded theory approach.ResultsWe identified seven types of variability (e.g., functionality, infrastructure) and eight mechanisms to handle variability (e.g., add-ons, code switches).ConclusionsWe provide generic types for classifying variability in enterprise software systems, and reusable mechanisms for handling such variability. Some variability types and handling mechanisms for enterprise software systems found in the real world extend existing concepts and theories. Others confirm findings from previous research literature on variability in software in general and are therefore not specific to enterprise software systems. Our findings also offer a theoretical foundation for describing variability handling in practice. Future work needs to provide more evaluations of the theoretical foundations, and refine variability handling mechanisms into more detailed practices.     

Building a research network to better understand climate governance in the Great Lakes

Climate-driven disturbances threaten the sustainability of coastal communities in the Great Lakes Basin. Because such disturbances are unpredictable, their magnitude, number and intensity are changing, and they occur at varying temporal and spatial scales. Consequently, communities struggle to respond in effective ways. The expected intensification of climate-driven disturbances will require that community capacity and governance structures match the spatial and temporal scales of these disturbances, as the most sustainable social and economic systems will be those that can respond at similar frequencies to key natural system drivers. The Climate Governance Variability in the Great Lakes Research Coordination Network (CGVG-RCN) was recently established to address questions about the relationship between climate-driven disturbances and community response. The objective of this short communication is to introduce the ideas behind the CGVG-RCN, outline its goals, and facilitate engagements and collaboration with social and natural scientists interested in social-ecological systems in the Great Lakes Basin.     

BIM perspective definition metadata for interworking facility management data

The purpose of this research is to suggest and develop a building information modeling (BIM) database based on BIM perspective definition metadata for connecting external facility management (FM) and BIM data, which considers variability and expandability from the user’s perspective. The BIM-based FM system must be able to support different use cases per user role and effectively extract information required by the use cases from various heterogeneous data sources. If the FM system’s user perspective becomes structurally fixed when developing the system, the lack of expandability can cause problems for maintenance and reusability. BIM perspective definition (BPD) metadata helps increase expandability and system reusability because it supports system variability, which allows adding or changing the user perspective even after the system has been developed. The information to be dealt with differs according to the user’s role, which also means that the data model, data conversion rules, and expression methods change per perspective. The perspective should be able to extract only the user-requested data from the heterogeneous system’s data source and format it in the style demanded by the user. In order to solve such issues, we analyzed the practice of FM and the benefits of using BIM-based FM, and we proposed a BPD that supports data extraction and conversion and created a prototype.     

Questionnaire-based variability modeling for system configuration

Variability management is a recurrent issue in systems engineering. It arises for example in enterprise systems, where modules are configured and composed to meet the requirements of individual customers based on modifications to a reference model. It also manifests itself in the context of software product families, where variants of a system are built from a common code base. This paper proposes an approach to capture system variability based on questionnaire models that include order dependencies and domain constraints. The paper presents analysis techniques to detect circular dependencies and contradictory constraints in questionnaire models, as well as techniques to incrementally prevent invalid configurations by restricting the space of allowed answers to a question based on previous answers. The approach has been implemented as a toolset and has been used in practice to capture configurable process models for film post-production.

Analyzing evolution of variability in a software product line: From contexts and requirements to features

Context

In the long run, features of a software product line (SPL) evolve with respect to changes in stakeholder requirements and system contexts. Neither domain engineering nor requirements engineering handles such co-evolution of requirements and contexts explicitly, making it especially hard to reason about the impact of co-changes in complex scenarios.

Objective

In this paper, we propose a problem-oriented and value-based analysis method for variability evolution analysis. The method takes into account both kinds of changes (requirements and contexts) during the life of an evolving software product line.

Method

The proposed method extends the core requirements engineering ontology with the notions to represent variability-intensive problem decomposition and evolution. On the basis of problemorientation, the analysis method identifies candidate changes, detects influenced features, and evaluates their contributions to the value of the SPL.

Results and Conclusion

The process of applying the analysis method is illustrated using a concrete case study of an evolving enterprise software system, which has confirmed that tracing back to requirements and contextual changes is an effective way to understand the evolution of variability in the software product line.     

Managing requirements specifications for product lines - An approach and industry case study

Software product line development has emerged as a leading approach for software reuse. This paper describes an approach to manage natural-language requirements specifications in a software product line context. Variability in such product line specifications is modeled and managed using a feature model. The proposed approach has been introduced in the Swedish defense industry. We present a multiple-case study covering two different product lines with in total eight product instances. These were compared to experiences from previous projects in the organization employing clone-and-own reuse. We conclude that the proposed product line approach performs better than clone-and-own reuse of requirements specifications in this particular industrial context.     

Comparison between Two Spectral Mixture Analysis Methods based on IDL and MATLAB

Interactive Data Language (IDL) is a language in the development of application based on multiplatform and object\|oriented,which has significant advantages in data analysis and visualization.The MATLAB is a software with powerful features in the image of processing and programming in complex numerical analysis,which based on matrix calculation.Programming in combining the IDL with MATLAB,meanwhile using the Extended Linear Mixed Model for endmember unmixing in Hyperion images.To verify the results of endmember unmixing,adopted the Fully Constrained Least Squares for comparative analysis.The results showed that：The method of programming in combining the IDL with MATLAB not noly possess the advantages of both but also enhance the efficiency in programming,it is conducive to remote sensing image processing.Meanwhile endmember unmixing results show that：the Extended Linear Mixing Model unmixing has a higher accuracy when the proportion of local category in the image is large.On the contrary,the Fully Constrained Least Squares unmixing has a higher accuracy.     

基于RVA法的河流生态需水量研究

以河流生态水文变化的指标体系（Indicator of Hydrologic Alteration,IHA）为基础,立足整体河流水文情势的流量谱系,采用变异性范围法（Range of Variability Approach,RVA）,进行基于长系列历史流量资料的河流生态需水量估算,并以山西省5条较大河流的生态敏感断面为对象进行研究。结果表明,利用本方法估算的结果是合理的,可为河流生态需水量的估算和水资源的可持续开发利用提供科学依据。     

Simulator-mediated acquisition of a dynamic control skill

Uses of stored skill-models to accelerate simulator-based real-time training in a control skill are discussed. A real-time coach must deliver advice at three levels: (1) what to do next, (2) what to watch for, and (3) what went wrong. Human learning and machine learning results are presented using different screen representations of a pole-and-cart balancing task.     

Dynamic adaptation of service compositions with variability models

Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using Constraint Programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime.