A Proactive Support for Conceptual Interoperability Analysis in Software Systems

Abukwaik, Hadil; Naab, Matthias; Rombach, Dieter

doi:10.1109/wicsa.2015.17

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…The presented work in this article is based on the Conceptual Interoperability Constraints (COIN) Model [3], which focuses on conceptual and architectural restrictions of interoperable software systems and can be applied to different system types (e.g., information systems, embedded systems, mobile systems, etc.). As COINs govern the software systems interoperability with other systems, missing or wrongly understanding them may defect the desired interoperability by leading to conceptual inconsistencies or meaningless results.…”

Section: Conceptual Interoperability Constraintsmentioning

confidence: 99%

“…Wu et al [6] targeted parameters dependency constraints, Pandita et al [16] inferred formal specifications for methods pre/post conditions, and Zhong et al [17] recognized resource specifications. We complement these works and elaborate on Abukwaik et al [3] idea of extracting a comprehensive set of conceptual interoperability constraints.…”

Section: Related Workmentioning

confidence: 99%

“…We define the Conceptual interoperability constraints (COINs) as the characteristics that control the meaningful exchange of data and functions between systems. We classify these constraints into the following classes: Syntax, Semantics, Structure, Dynamics, Context, and Quality [3]. For example, a contextual COIN restricts an offered sport-related software functionality to process data from elderly users.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we extend our proposed idea of utilizing machine learning techniques to identify the COINs in the text of API documents (published in [7] as a component of our analysis framework [3]). That is, we focus on supporting the first step of the conceptual interoperability analysis.…”

Section: Introductionmentioning

confidence: 99%

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How practical is it? Machine Learning for Identifying Conceptual Interoperability Constraints in API Documents

Abukwaik,

Abufouda,

Nair

et al. 2018

Preprint

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Building meaningful interoperation with external software units requires performing the conceptual interoperability analysis that starts with identifying the conceptual interoperability constraints of each software unit, then it compares the systems' constraints to detect their conceptual mismatch. We call the conceptual interoperability constraints (the COINs) that can be of different types including structure, dynamic, and quality. Missing such constraints may lead to unexpected mismatches, expensive resolution, and running-late projects. However, it is a challenging task for software architects and analysts to manually analyze the unstructured text in API documents to identify the COINs. Not only it is a tedious and time-consuming task, but also it needs knowledge about the constraint types. In this article, we present and evaluate our idea of utilizing machine learning techniques in automating the COIN identification, which is the first step of conceptual interoperability analysis, from human text in API documents. Our empirical research started with a multiple-case study to build the ground truth dataset, on which we contributed our machine learning COIN-Classification Model. We show the model's robustness through experiments using different machine learning text-classification algorithms. The experiments' results revealed that our model can achieve up to 87% accuracy in automatically identifying the COINs in text. Thus, we implemented a tool that embeds our model to demonstrate its practical value in industrial context. Then, we evaluated the practitioners' acceptance for the tool and found that they significantly agreed on its usefulness and ease of use.

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Section: Conceptual Interoperability Constraintsmentioning

confidence: 99%