DMCA
Better Cross Company Defect Prediction
| Citations: | 11 - 0 self |
BibTeX
@MISC{Peters_bettercross,
author = {Fayola Peters and Tim Menzies and Andrian Marcus},
title = {Better Cross Company Defect Prediction},
year = {}
}
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Abstract
Abstract—How can we find data for quality prediction? Early in the life cycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to the local project. But is this the best approach? This paper introduces the Peters filter which is based on the following conjecture: When local data is scarce, more information exists in other projects. Accordingly, this filter selects training data via the structure of other projects. To assess the performance of the Peters filter, we compare it with two other approaches for quality prediction. Withincompany learning and cross-company learning with the Burak filter (the state-of-the-art relevancy filter). This paper finds that: 1) within-company predictors are weak for small data-sets; 2) the Peters filter+cross-company builds better predictors than both within-company and the Burak filter+cross-company; and 3) the Peters filter builds 64 % more useful predictors than both withincompany and the Burak filter+cross-company approaches. Hence, we recommend the Peters filter for cross-company learning. Index Terms—Cross company; defect prediction; data mining I.
Keyphrases
peter filter better cross company defect prediction cross-company learning quality prediction information exists state-of-the-art relevancy filter following conjecture cross-company build burak filter cross-company local data defect prediction prior work within-company predictor life cycle burak filter data mining burak filter cross-company approach useful predictor index term cross company withincompany learning local project small data-sets relevant training data
