Recommending Relevant Code Artifacts for Change Requests Using Multiple Predictors

  • Name:

    Konferenzartikel 

  • 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.
  • Year:

    2012 

Beteiligte Mitarbeiter (zufällige Reihenfolge)
Titel Vorname Nachname
Dipl.-Inform. Oliver Denninger

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.},
}