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Analysis and Improvement of Agile Software Development Process Using Process Mining and Machine Learning Algorithms

Fardipour Asl, Mohsen | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57552 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Hassan Nayebi, Erfan
  7. Abstract:
  8. In the competitive world of software development, IT product development teams face numerous challenges and issues within business processes. In recent years, the combination of process mining and machine learning algorithms to optimize software development processes has gained attention. However, there is still a lack of a comprehensive model for identifying and conducting root-cause analysis of problems and providing solutions based on them in agile software development systems. The aim of this research is to find a solution for identifying and addressing issues in the software development process by modifying the change request workflow and examining various aspects of incidents. To achieve this goal, process mining was applied to the change request workflows, and clustering was used to better analyze around ten thousand recorded event reports. Discovery of the change request workflow was conducted using the Alpha and Heuristic algorithms, and process analysis was performed through diagrams and statistical reports. Event titles were scored using the TF-IDF technique, and clustering based on the incident resolution time was carried out using the K-means technique. The output from process mining and clustering was examined using root-cause analysis documentation and expert interviews. Based on the conditions and analysis results, the "software engineer in an operational environment" solution, along with the integration of a customized Scrum framework and extreme programming, was proposed. In the case study conducted, after implementing this method, a survey of stakeholders and system experts indicated that the solution led to improvements in the process and a 21% increase in stakeholder satisfaction
  9. Keywords:
  10. Root Causes ; Process Mining ; Agile Software Development ; Root Cause Analysis Document ; Incident Report Management ; Change Request Management ; Term Frequency-Inverse Document Frequency (TF-IDF)Method

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