Automatically Matching Requirements to Process Models Determining the Impact of Change (ACCHA)

Target audience: Computer Science students with a data science/machine learning/NLP background.

Task description: The main task of this Master thesis is to develop and implement a technique that is able to automatically link textual requirements to model-based representations of business process (so-called process models). By doing so, it will be possible to quantify the impact of a set of requirements (e.g. specifying a new piece of software) on the currently running business processes of an organization. The main challenge associated with this task is to successfully leverage Natural Language Processing techniques to deal with rather short language fragments that are typically used in process models. Real-world data will be provided for both developing and testing the technique.

Context

This work is part of the pioneering activities of the start-up company ACCHA. The goal of ACCHA is to replace physical business analysts with Artificial Intelligence-based software. Instead of having humans analyze requirements for several weeks, the vision is to let ACCHA do this is in a couple of seconds. The Master thesis candidate will become part of the ACCHA team and participate in the journey of ACCHA towards becoming a successful organization. The ACCHA office is located at the TU/e campus.

Required Skills

  • Knowledge of business process management, in particular of process modeling languages (Petri nets, BPMN)
  • Experience with programming in Python
  • Experience with working in an Agile development environment
  • Experience with Git
  • Fluency in English
  • Any background in Machine Learning / Natural Language Processing is a plus

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