Abstract
Many software development projects use work items such as tasks or bug reports to describe the work to be done. Some projects allow end-users or clients to enter new work items. New work items have to be triaged. The most important step is to assign new work items to a responsible developer. There are existing approaches to automatically assign bug reports based on the experience of certain developers based on machine learning. We propose a novel model-based approach, which considers relations from work items to the system specification for the assignment. We compare this new approach to existing techniques mining textual content as well as structural information. All techniques are applied to different types of work items, including bug reports and tasks. For our evaluation, we mine the model repository of three different projects. We also included history data to determine how well they work in different states.
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Helming, J., Arndt, H., Hodaie, Z., Koegel, M., Narayan, N. (2011). Automatic Assignment of Work Items. In: Maciaszek, L.A., Loucopoulos, P. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2010. Communications in Computer and Information Science, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23391-3_17
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DOI: https://doi.org/10.1007/978-3-642-23391-3_17
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