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Towards Cross-Project Defect Prediction with Imbalanced Feature Sets

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Peng He, Bing Li, Yutao Ma

Software Engineering · Submitted 16 Nov 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Besides, the empirical results indicate that the hybrid model composed of CPDP and CPDP-IFS does improve the prediction performance of the regular CPDP to some extent.

how it is stated · hedged possibility 0.69 read the record →

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arXiv ID1411.4228 (version 1)
Pith IDX2C3TPFG, Pith's permanent record of this paper and its review
CitationBing Li, Peng He, Yutao ma. "Towards Cross-Project Defect Prediction with Imbalanced Feature Sets." arXiv:1411.4228 (2014). https://pith.science/paper/X2C3TPFG
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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,355 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.59 · human participants 0.03 · controlled comparison 0.86 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.86 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Cross-project defect prediction (CPDP) has been deemed as an emerging technology of software quality assurance, especially in new or inactive projects, and a few improved methods have been proposed to support better defect prediction. However, the regular CPDP always assumes that the features of training and test data are all identical. Hence, very little is known about whether the method for CPDP with imbalanced feature sets (CPDP-IFS) works well. Considering the diversity of defect data sets available on the Internet as well as the high cost of labeling data, to address the issue, in this paper we proposed a simple approach according to a distribution characteristic-based instance (object class) mapping, and demonstrated the validity of our method based on three public defect data sets (i.e., PROMISE, ReLink and AEEEM). Besides, the empirical results indicate that the hybrid model composed of CPDP and CPDP-IFS does improve the prediction performance of the regular CPDP to some extent.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Cross-project defect prediction (CPDP) has been deemed as an emerging technology of software quality assurance, especially in new or inactive projects, and a few improved methods have been proposed to support better defect prediction. background 1.00 0.05 0.03 asserted 0.94
s1 However, the regular CPDP always assumes that the features of training and test data are all identical. background 0.56 0.05 0.02 asserted 1.00
s2 Hence, very little is known about whether the method for CPDP with imbalanced feature sets (CPDP-IFS) works well. gap 1.00 0.06 0.03 asserted 0.81
s3 Considering the diversity of defect data sets available on the Internet as well as the high cost of labeling data, to address the issue, in this paper we proposed a simple approach according to a distribution characteristic-based instance (object class) mapping, and demonstrated the validity of our method based on three public defect data sets (i.e., PROMISE, ReLink and AEEEM). contribution 0.64 0.95 0.10 asserted 1.00
s4 Besides, the empirical results indicate that the hybrid model composed of CPDP and CPDP-IFS does improve the prediction performance of the regular CPDP to some extent. result 0.99 0.95 0.76 hedged possibility 0.69

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from analysis of existing data 0.60.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Cross-project defect prediction (CPDP) has been deemed as an emerging technology of software quality assurance, especially in new or inactive projects, and a few improved methods have been proposed to support better defect prediction. However, the regular CPDP always assumes that the features of training and test data are all identical. Hence, very little is known about whether the method for CPDP with imbalanced feature sets (CPDP-IFS) works well. Considering the diversity of defect data sets available on the Internet as well as the high cost of labeling data, to address the issue, in this paper we proposed a simple approach according to a distribution characteristic-based instance (object class) mapping, and demonstrated the validity of our method based on three public defect data sets (i.e., PROMISE, ReLink and AEEEM). Besides, the empirical results indicate that the hybrid model composed of CPDP and CPDP-IFS does improve the prediction performance of the regular CPDP to some extent.

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