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