Recommending Relevant Code Artifacts for Change Requests Using Multiple Predictors

Publikationstyp
Konferenz
Autor(en)
Oliver Denninger
Jahr
2012
Seiten
78-79
Monat
June
Adresse
Zurich
Buchtitel
Recommendation Systems for Software Engineering (RSSE), 2012 Third International Workshop on
Abstract
Finding code artifacts affected by a given change request is a time-consuming process in large software systems. Various approaches have been proposed to automate this activity, e.g., based on information retrieval. The performance of a particular prediction approach often highly depends on attributes like coding style or writing style of change request. Thus, we propose to use multiple prediction approaches in combination with machine learning. First experiments show that machine learning is well suitable to weight different prediction approaches for individual software projects and hence improve prediction performance.
DOI
10.1109/RSSE.2012.6233416
Forschungsfelder
Software- und Architekturanalyse, Geschäftsprozess- und Softwaremanagement
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Eingetragen von
Oliver Denninger