An Empirical Study on Data-driven Requirements Elicitation : Reflections from Nordic Enterprises

• There is a plethora of digital data sources that may be exploited for collecting requirements for system development and evolution. In contrast to human sources, i.e. stakeholders, digital sources continuously generate data that is often not originally created for the purposes of requirements elicitation, e.g. on forums, microblogs, machine-generated trace logs, and sensor data. Streams of large volumes of data can be exploited to enable automation of a continuous requirements elicitation process using AI techniques that combine natural language or machine data processing, with machine learning. On the other hand, the complex characteristics of big data due to its size, lack of structure, high dynamics, and low predictability, present numerous challenges on the process of extracting requirements-related information that would be of a clear value for companies. The purpose of this interview study was to, from the practitioners’ perspective, elicit their overall expectations and needs for a method for the elicitation of system requirements from digital data sources. Semi-structured interviews were conducted with several industrial experts from different business domains and the collected empirical data has been analyzed using thematic analysis. The results lead to the identification of a set of hig-hlevel requirements related to the method for the elicitation from digital data sources.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Nyckelord

Data-driven Requirements Engineering

Big Data

Requirements Elicitation

Agile Requirements Engineering

Enterprise Modeling

data- och systemvetenskap

Computer and Systems Sciences

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)