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Automatic Detection of Employee Interaction Anti-Patterns in Agile Environments

Farsian, Mojtaba | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56907 (01)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Habibi, Moslem
  7. Abstract:
  8. Today, the digital transformation has increasingly confronted software development companies with numerous requests for the production of custom software. Over time, these companies have realized that paying attention only to the technical aspect is not enough for delivering a product that functions correctly; the social dimension must also be considered. Consequently, many agile software development methods have been developed. However, an important point to note is that implementing and maintaining these methods involves many intricacies, with the most significant being the management of interpersonal relationships. With the continuous expansion of technology and the advancement of communication tools, the volume of data produced in organizations has significantly increased. Among these, organizational chats are recognized as one of the most important sources of this data. These messengers are not only carriers of daily work communications but can also be a rich source of useful information about trends, needs, and internal organizational challenges. Optimal use of these communication data can help managers gain a deeper understanding of their work environment and make more strategic decisions. Analyzing organizational chat data can aid in identifying patterns of collaboration, prioritizing discussion topics, and even predicting future organizational needs. Additionally, a better understanding of how employees communicate and interact can be achieved, which can lead to improved work environments and increased productivity. In this research, social network analysis and graph analysis methods will be used to identify patterns that have been mistakenly created and, as much as possible, to prevent the formation of these harmful patterns in order to produce higher quality products. The method presented in this research covers everything from the automatic extraction of graphs to the identification of some of these harmful patterns. This graph is extracted through the analysis of organizational chat software, and the results are obtained from the analysis of a corporate software graph
  9. Keywords:
  10. Agility ; Communications ; Interaction ; Antipattern ; Social Network Analysis ; Automatic Detection

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