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Recommending Relevant Code Artifacts for Change Requests Using Multiple Predictors

Recommending Relevant Code Artifacts for Change Requests Using Multiple Predictors
Name:

Konferenzartikel 

Year:

2012 

Author:

Oliver Denninger 

Zusammenfassung

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.

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Bibtex

@inproceedings{,
author={Oliver Denninger},
title={Recommending Relevant Code Artifacts for Change Requests Using Multiple Predictors},
year=2012,
month=Jun,
booktitle={Proceedings of the 3rd International Workshop on Recommendation Systems for Software Engineering (RSSE'12)},
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.},