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Stacking regularization in analogy-based software effort estimation.

Anupama Kaushik1, Prabhjot Kaur1, Nisha Choudhary1

  • 1Maharaja Surajmal Institute of Technology, GGSIP University, New Delhi, India.

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Summary
This summary is machine-generated.

This study introduces SABE, a novel stacking regularization method to enhance analogy-based estimation (ABE) for software projects. SABE improves effort prediction accuracy by combining multiple models for more reliable software effort estimation.

Keywords:
Analogy-based estimationMachine learningSoftware effort estimationStacking

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Predictive Analytics

Background:

  • Analogy-based estimation (ABE) relies on past project data for effort prediction.
  • Existing ABE solution functions have limitations in prediction accuracy.
  • Enhancing ABE is crucial for effective software project management.

Purpose of the Study:

  • To propose a new solution function, SABE (Stacking regularization in Analogy-Based Estimation), to improve ABE accuracy.
  • To leverage stacking, a machine learning technique, for enhanced software effort estimation.
  • To validate the effectiveness of SABE against existing ABE methods.

Main Methods:

  • Developed SABE, incorporating stacking regularization for ABE.
  • Utilized stacking to combine multiple models, harnessing their collective predictive power.
  • Validated SABE on four diverse software effort estimation datasets.

Main Results:

  • SABE demonstrated improved performance across key evaluation metrics.
  • Compared to traditional methods like closest analogy, mean, median, and inverse distance weighted mean, SABE showed promising results.
  • The proposed method achieved better accuracy in software effort prediction.

Conclusions:

  • SABE offers a significant advancement in analogy-based software effort estimation.
  • Stacking regularization is an effective approach to enhance prediction accuracy in ABE.
  • The findings suggest SABE as a valuable tool for more reliable software project effort forecasting.