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Bottleneck Analysis in Software Development: A Case Study Using Process Mining
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AI
DOI：10.1007/978-3-032-03708-4_22.png)
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Abstract
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Bottleneck analysis in software development highlights inefficiencies that hinder team productivity. This study uses Process Mining techniques to identify and address bottlenecks in workflows by analyzing event logs from IDEs and task management systems. The approach involves process discovery, conformance checking, and predictive modeling to detect inefficiencies and deviations. A predictive model was developed to anticipate future bottlenecks, allowing for targeted interventions. Major bottlenecks included delays in communication, redundant task cycles, and misallocated resources. Workflow adjustments, such as process redesign and task redistribution, led to a 38% improvement in efficiency overall like reduction of queue time. The study highlights the role of Process Mining in optimizing development workflows, with a focus on data quality and system integration for accurate analysis.
Keywords:
Process Mining
Bottleneck Analysis
Workflow Optimization
Process Efficiency
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