Data-Driven Optimization of Bug-to-Expert Assignment in Software Development

Purpose: The aim of this study is to use the data of past projects and align the skills of employees with the needs of certain bugs, thus enhancing the quality of outputs and reducing time taken to find solutions to a specific issue. To build a universal, competency-based strategy of bug assignment, the research also tends to create a framework, which could be extended to other projects. Methodology: The data of one of the privately held companies that are under analysis includes important project items such as Bug ID, Product, Component, Assignee, Priority, Severity, and Skill Level. These are essential aspects that are analyzed to identify trends that can lead to a smarter allocation process. Data-driven allocation strategies are created with the help of pattern mining rules and machine-learning algorithms used to disclose the hidden correlations between characteristics. Its methodology is directed at the aligning of personnel talents and the difficulty of the task within the complex decision-making environment based on the analysis of previous information in the software development projects.