Effectiveness of auto-sklearn in Software Bug Prediction

Kazuya Tanaka, Akito Monden, Yucel Zeynep

Research output: Contribution to journalArticlepeer-review


Auto-sklearn is a recent attention-gathering software library for automated machine learning that can au-tomatically select appropriate prediction models and hyper parameters for a given data set. In this paper we empirically evaluate the effectiveness of auto-sklearn in software bug prediction. In the experiment, we used software metrics of 20 OSS projects for inter-version bug prediction and compared auto-sklearn with random forrest, decision tree and linear descriminat analysis by using AUC of ROC curve as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forrest. We conclude that, although auto-sklearn is useful for bug prediction, we cannot expect better prediction performance than conventional modeling techniques.

Original languageEnglish
Pages (from-to)46-52
Number of pages7
JournalComputer Software
Issue number4
Publication statusPublished - Jan 1 2019

ASJC Scopus subject areas

  • Software


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