What Requirements Engineering can Learn from Process Mining

Abstract

Process Mining is an approach that uses event logs of systems or processes and turns them into valuable insights. The main characteristic of process mining techniques is that they focus on and exploit "real behavior" of a large number of stakeholders of a system or of a process. On the other hand, requirements engineering is concerned with requirements elicitation and analysis not only in terms of software specifications but also in terms of activities carried out within an organizational and social context. Furthermore, involving a large number of users/stakeholders has always been a challenge with traditional requirements engineering methods. Although both requirements engineering and process mining have gained increasing research attention, the synergy between these two domains is yet to be exploited. Such a synergy can help both domains benefit from their capabilities and mitigate their own challenges. The ability of process mining to exploit huge data logs can help requirements engineers cope with the above challenge. This paper aims to highlight how requirements engineering can benefit from process mining's components such as execution logs, process discovery and conformance techniques for requirements elicitation, prioritization and validation.

REBD: A Conceptual Framework for Big Data Requirements Engineering

“…The AUC diagrams enhance the users' experience, optimize the systems' utility, and consequently maximize profit. Process mining, described in [19], is another efficient method that helps SRE to elicit, prioritize, and validate requirements from big data using execution logs, process discovery, and conformance techniques. The capability of process mining to discover valuable insights from event logs and processes of the system helps SRE to eradicate many challenges of traditional RE.…”

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“…Process mining is another efficient method to discover business values from the existing big data [14][15][21][22]. The research done in [14], explains how to turn the event logs into valuable insights, and the result of this research are used to identify and understand the deviations, risks, bottlenecks, and inefficiencies involved.…”

“…The research done in [14], explains how to turn the event logs into valuable insights, and the result of this research are used to identify and understand the deviations, risks, bottlenecks, and inefficiencies involved. Process mining helps SRE to elicit, prioritize, and validate requirements from big data using execution logs, process discovery, and conformance techniques [15]. One of the challenges in process mining is monitoring and controlling the existing big data in the organizations.…”

“…Different data analysis technologies such as machine learning, deep learning, natural language processing, data mining, text analytics, and predictive analytics can be employed to analyze data and extract business values from it [12,[18][19][20]. Moreover, techniques like process mining [15] and tools like smart grids [28] can be employed to accelerate the process of knowledge discovery from big data.…”

“…Process mining is an advanced technology that not only can use for discovering business values from data but also can recognize the risks, deviations, bottlenecks, and inefficiencies associated with big data [14]. Process mining helps SRE to elicit, prioritize, and validate requirements from big data using execution logs, process discovery, and conformance techniques [15].…”