Systems Interoperability Types: A Tertiary Study: Introduction

Table of Links

Introduction

Related Work

Research Method

Results

Discussions

Conclusions & References

Interoperability has been a focus of attention over at least four decades, with the emergence of several interoperability types (or levels), diverse models, frameworks, and solutions, also as a result of a continuous effort from different domains. The current heterogeneity in technologies such as blockchain, IoT and new application domains such as Industry 4.0 brings not only new interaction possibilities but also challenges for interoperability.

Moreover, confusion and ambiguity in the current understanding of interoperability types exist, hampering stakeholders’ communication and decision making. This work presents an updated panorama of software-intensive systems interoperability with particular attention to its types. For this, we conducted a tertiary study that scrutinized 37 secondary studies published from 2012 to 2023, from which we found 36 interoperability types associated with 117 different definitions, besides 13 interoperability models and six frameworks in various domains.

This panorama reveals that the concern with interoperability has migrated from technical to social-technical issues going beyond the software systems’ boundary and still requiring solving many open issues. We also address the urgent actions and also potential research opportunities to leverage interoperability as a multidisciplinary research field to achieve low-coupled, cost-effective, and interoperable systems.

Additional Key Words and Phrases: Interoperability type, Interoperability model, Interoperability framework, Tertiary study

1 INTRODUCTION

Software-intensive systems (SIS) have increasingly resulted from the combination of multiple heterogeneous systems that interoperate suitably and transparently among them to provide complex functionalities, attending users and business needs. In short, SIS are composed of individual systems that need to exchange information to fulfill specific purposes, and their operations depend sometimes on the interactions among heterogeneous, distributed, independent, and large-scale systems [6, 16]. These systems have supported large infrastructures and application domains, such as Industry 4.0, Health 4.0, smart cities, smart farms, and transportation, just to mention a few.

For these systems, interoperability is a key factor that assures their existence (i.e., they would not exist without such interoperability). In turn, interoperability refers to the ability of two or more systems or components to exchange information and use it for a given purpose [43].

Interoperability also refers to the process of communication among systems or components without generating a technological dependency among them [95]. Moreover, interoperability is not only about systems or network integration but also about other dimensions, such as social [18] and legal interoperability [30], going nowadays beyond the boundary of software systems.

Hence, interoperability can be considered a multidimensional concept comprising diverse perspectives coming from different research communities and application domains.

Interoperability has been a challenge since monolith systems started to communicate with others, and distributed systems started to become popular.

From middleware, such as CORBA (Common Object Request Broker Architecture)[1] that enabled communication among systems written in different languages and running on different platforms and hardware, to newer solutions, such as API (Application Programming Interface) and web services, a variety of interoperability types and solutions has emerged to deal with the heterogeneity of systems, including systems from different companies and built with diverse technologies, languages, platforms, standards, communication protocols, and even deployed in diverse locations like cloud and mobile devices.

Efforts to mitigate the challenges of interoperability include specific solutions for a given domain (e.g., interoperability standards for Industry 4.0 [19]) or theoretical contributions (e.g., an analysis of pragmatic interoperability [7]).

To address the complicated issues associated with interoperability and better understand it, interoperability has been broken down over the years into various types (often referred to as interoperability levels). Some well-known types are technical, syntactic, and semantic. Each type deals with specific barriers to be overcome to make possible systems communication.

The increasing number of interoperability types hampers a comprehensive view of those similar or different types, those related among them, and even a wider acceptation of their definitions/understandings.

Hence, various classifications of interoperability types (also referred to as interoperability models) emerged, such as LCIM (Levels of Conceptual Interoperability Model) [86] and LISI (Levels of Information Systems Interoperability) [22]. Additionally, interoperability frameworks, such as EIF (European Interoperability Framework) [30], that usually encompass interoperability models, principles, guidelines, and even reference architectures also exist.

Although a considerable number of initiatives and solutions to mitigate interoperability problems are available, interoperability is still a big challenge for software projects, particularly for those contemporary, large, and critical domains.

Regarding the three closely related works, they are outdated ([33] published in 2007) or address specific classes of systems (e.g., context-awareness systems [63]) or a given scenario (a comparative study of interoperability assessment models [52]). To the best of our knowledge, an updated and comprehensive panorama of interoperability types (or levels) is still missing.

At the same time, several new types have emerged, while others have become outdated, leading to confusion and ambiguity in the current understanding of the interoperability types. In turn, interoperability types has usually guided the development of models such as LCIM and LISI and frameworks such as EIF as in the past, but the lack of a common and updated understanding about them can difficult future efforts.

In summary, the main real-world problem to be addressed in this work is the existing barrier due to the misunderstanding about interoperability types that has hampered interoperability solutions and research and, ultimately, has led to extra effort and cost to build SIS. In this scenario, this paper yields six main contributions:

• We offer an updated, original, and comprehensive panorama of interoperability types that was rigorously defined through the conduction of a tertiary study, which scrutinized 37 literature reviews, including systematic literature reviews (SLR) and systematic mappings (SM). In turn, a tertiary study can systematically gather evidence and synthesize data and information from secondary studies in a specific area [50, 91].

• We provide a classification of the 36 interoperability types found in our tertiary study and the existing similarities, overlaps, and differences among them. We also show the evolution of these types over the years and the new types that emerged more recently, reflecting the state-of-the-art scenario of the field.

• We collect 13 interoperability models (six conceptual models and seven interoperability assessment models (IAM)), when they emerged, their evolution over the years, the existing relations among them, and their interoperability types (or levels). In short, conceptual models encompass interoperability types and relationships among them that are usually hierarchical, while IAM include interoperability types, barriers, achievement levels, and means to measure and evolve interoperability [76]. These models are the all existing ones, as we systematically scrutinized the literature when conduction the tertiary study.

• We gathered six interoperability frameworks, which refer to a more complete way (compared to interoperability models and IAM) to deal with interoperability issues and achieve different purposes. These seven frameworks refer to possibly all existing, as we also systematically and carefully looked for them during the conduction of our study.

• We found the 11 main domains that have concerned with interoperability. We also discovered various categories of interoperability solutions (including ontologies, platforms, and reference architectures) proposed for these different domains.

• Based on our tertiary study, we present eight main findings and the open issues associated with each finding as well. We also offer nine future actions and potential research opportunities to be somehow urgently performed.

Besides improving the communication among of stakeholders (who are interested in SIS interoperability issues), this work foresees implications for researchers and practitioners:

• Implications for researchers: Considering that SIS interoperability is still challenging, many opportunities for research exist. With the emergence of new interoperability types, there is a need for further investigation of new interoperability models and frameworks, new relationships among existing types and new ones, and exploration of new methodsto evaluate interoperability solutions. Additionally, given the multidisciplinary nature of interoperability, researchers need to examine how social-technical issues (for instance, cultural interoperability) impact interoperability beyond technical aspects.

• Implications for practitioners: By providing a clear panorama of all interoperability types, this work can support industry decision-making regarding the design, implementation, and evaluation of interoperability solutions (e.g., interoperability standards, platforms (tools and services), and ontologies). Furthermore, this work can guide practitioners in identifying specific interoperability types relevant to their industry projects and domains, creating interoperable SIS tailored to their particular needs.

It is worth highlighting that our previous work (a symposium paper) offered a preliminary view of systems interoperability focused on its types [78]. This current work differs from that by answering different research questions, covering a more extensive period, discussing the results more deeply, summarizing the main findings and related open issues, and offering urgent research topics for future research.

This work is organized as follows: Section 2 summarizes other tertiary studies in software engineering and the related works that investigated interoperability from a broader view. Next, Section 3 describes the research method used in our work. The planning and execution phases are detailed, including the research questions, search strategy, inclusion and exclusion criteria, and quality assessment criteria; finally, it lists the studies selected in our tertiary study.

Section 4 shows the results of this study; particularly, it presents an overview of studies in Section 4.1 and details the interoperability types, their definitions, similarities, overlapping, and their classification illustrated through a graphical representation in Section 4.2.

Next, Section 4.3 offers the interoperability models and frameworks and depicts the interoperability types (or levels) they encompass, as well as their evolution over the years. Section 4.4 addresses the domains that have concerned with interoperability, e.g., Industry 4.0 and health, and also diverse interoperability solutions found for these domains.

Section 5 provides a discussion on the results achieved; more specifically, it presents the main findings and open issues in Section 5.1 and the future actions and potential research opportunities that can further support SIS interoperability in Section 5.2. Besides, Section 5.3 presents the threats to the validity of this work and the means used to mitigate them. Finally, Section 6 concludes this work.

[1] https://https://www.corba.org//