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RELMP-MM: an approach to cross project fault prediction using improved regularized extreme learning machine and identical matched metrics

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Abstract

Cross project fault prediction (CPFP) is a challenging issue in the software fault prediction (SFP) domain due to different data distributions in source and target datasets. To resolve this issue, we have proposed an efficient and improved version of the existing regularized extreme learning machine (RELM), we call it as RELM Plus. The proposed RELM Plus model is further extended using the concept of matched metrics to predict the number of software faults on cross-project data, we call it as RELMP-MM model. The proposed RELMP-MM model selects the source dataset corresponding to the given target dataset based on the number of identical matched metrics. Then, the proposed model predicts the number of software faults on the given target dataset. In this paper, we have considered both within project fault prediction (WPFP) as well as CPFP. The proposed model is validated using twenty-five public datasets. The experimental results along with the statistical analysis show that the proposed RELMP-MM model performs significantly better as compared to existing state of the art models. It shows an improvement of at least 8% to 13% in terms of Average Absolute Error (AAE) and 7% to 12% in terms of Average Relative Error (ARE).

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Data availibility statement

The datasets generated during or analysed during the current study are available in the [PROMISE] repository, [www.github.com/klainfo/DefectData].

Notes

  1. Ordinary Least Square.

  2. We have used samples and modules interchangeably in this paper. Both represent the same meaning.

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Acknowledgements

“Authors are thankful to the SERB, Government of India for project funding under VAJRA Scheme. We are thankful to the editor and anonymous reviewers for valuable feedback.”

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Pravas Ranjan Bal & Sandeep Kumar

Authors
  1. Pravas Ranjan Bal
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  2. Sandeep Kumar
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Correspondence to Sandeep Kumar.

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Appendices

Appendices

List of matched source and target datasets

The complete list of matched source and target datasets for the CPFP scenario as generated using Matched Metrics process under RELMP-MM is given below:

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    Jedit 4.1 - Ant 1.3

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    Jedit 4.1 - Ant 1.4

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    Xalan 2.4 - Ant 1.5

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    Jedit 4.1 - Ant 1.6

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    Jedit 4.1 - Ant 1.7

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    Ivy 1.4 - Camel 1.0

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    Ant 1.4 - Camel 1.2

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    Ivy 1.4 - Camel 1.4

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    Ivy 1.4 - Camel 1.6

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    Ant 1.6 - Jedit 4.0

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    Ant 1.6 - Jedit 4.1

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    Ant 1.6 - Jedit 4.2

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    Ant 1.4 - Jedit 4.3

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    Log4j 1.1 - Synapse 1.0

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    Ant 1.5 - Synapse 1.1

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    Jedit 4.0 - Synapse 1.2

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    Ant 1.5 - Xalan 2.4

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    Ant 1.5 - Xalan 2.5

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    Ant 1.4 - Xalan 2.6

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    Ant 1.4 - Ivy 1.4

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    Jedit 4.0 - Ivy 2.0

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    Ant 1.4 - Log4j 1.0

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    Ant 1.4 - Log4j 1.1

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    Ivy 1.4 - Xerces 1.2

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    Ivy 1.4 - Xerces 1.3

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Bal, P.R., Kumar, S. RELMP-MM: an approach to cross project fault prediction using improved regularized extreme learning machine and identical matched metrics. J Ambient Intell Human Comput 14, 13523–13542 (2023). https://doi.org/10.1007/s12652-022-03820-1

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