Re-expressing Business Processes Information from Corporate Documents into Controlled Language

Losada, Bell Manrique; Zapata-Jaramillo, Carlos Mario; Burgos, Diego Alexander Tibaduiza

doi:10.1007/978-3-319-41754-7_37

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Many studies have lately been addressing the need to elicit requirements from digital sources. One research track has focused on eliciting requirements from static data sources, typically domain knowledge [13], e.g., business documents [14], various types of models [15], or from software repositories, e.g., in order to support the elicitation of quality requirements [16]; the elicitation of data-driven quality requirements was also the focus of another study [17], emphasizing that these are typically collected as goals and need further refinement toward tangible requirements requests. Another, more recent research track concerns requirements elicitation from more dynamic, online data sources, motivated by the increasing inflow of user feedback.…”

Section: Data-driven Requirements Elicitationmentioning

confidence: 99%

Holistic data-driven requirements elicitation in the big data era

Henriksson

Zdravković

2021

Softw Syst Model

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Digital transformation stimulates continuous generation of large amounts of digital data, both in organizations and in society at large. As a consequence, there have been growing efforts in the Requirements Engineering community to consider digital data as sources for requirements acquisition, in addition to human stakeholders. The volume, velocity and variety of the data make requirements discovery increasingly dynamic, but also unstructured and complex, which current elicitation methods are unable to consider and manage in a systematic and efficient manner. We propose a framework, in the form of a conceptual metamodel and a method, for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources that aims to support data-driven requirements elicitation and management. The usability of the framework is partially validated by an in-depth case study from the business sector of video game development.

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Section: Data-driven Requirements Elicitationmentioning

confidence: 99%

Holistic data-driven requirements elicitation in the big data era

Henriksson

Zdravković

2021

Softw Syst Model

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“…One research track has focused on eliciting requirements from static data sources, typically domain knowledge [10], e.g. business documents [11] or various types of models [12,13]. In some efforts, the goal is to convert existing requirements, elicited through conventional elicitation techniques, into some other form (e.g.…”

Section: Data-driven Requirements Elicitationmentioning

confidence: 99%

A Data-Driven Framework for Automated Requirements Elicitation from Heterogeneous Digital Sources

Henriksson

Zdravković

2020

Lecture Notes in Business Information Processing

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Increased digitalization and the pervasiveness of Big Data, along with vastly improved data processing capabilities, have led to the consideration of digital data as additional sources of system requirements, complementing conventional stakeholder-driven approaches. The volume, velocity and variety of these digital sources present numerous challenges which existing system development methods are unable to manage in a systematic and efficient manner. We propose a holistic and data-driven framework for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources for the purposes of requirements elicitation and management. The proposed framework includes a conceptualization in the form of a meta-model and a high-level process for its use; the framework is illustrated in a real case of an enterprise software.

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“…There have been numerous efforts to automate requirements elicitation from static data, i.e., data that are generated with a relatively low velocity and rarely updated. These efforts can be grouped according to the following three aims: (1) eliciting requirements from static domain knowledge (e.g., documents written in natural languages [4,5], ontologies [6,7], and various types of models, e.g., business process models [8], UML use cases and sequence diagrams [9]), (2) performing specific requirements engineering activities based on requirements that have been already elicited (e.g., requirements prioritization [10], classification of natural language requirements [11], management of requirements traceability [12], requirements validation [13], generation of a conceptual model from natural language requirements [14]), or (3) developing tools to enhance stakeholders' ability to perform requirements engineering activities based on static domain knowledge or existing requirements (e.g., tool-support for collaborative requirements prioritization [15] and requirements negotiation with rule-based reasoning [16]).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

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Re-expressing Business Processes Information from Corporate Documents into Controlled Language

Cited by 3 publications

References 12 publications

Holistic data-driven requirements elicitation in the big data era

Holistic data-driven requirements elicitation in the big data era

A Data-Driven Framework for Automated Requirements Elicitation from Heterogeneous Digital Sources

Data-Driven Requirements Elicitation: A Systematic Literature Review

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