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Related Experiment Video

Updated: Nov 27, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Software Requirements Classification Using Machine Learning Algorithms.

Edna Dias Canedo1, Bruno Cordeiro Mendes1

  • 1Department of Computer Science, University of Brasília (UnB), P.O. Box 4466, Brasília 70910-900, Brazil.

Entropy (Basel, Switzerland)
|December 8, 2020
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Summary
This summary is machine-generated.

This study compares text feature extraction techniques and machine learning algorithms for classifying software requirements. Term Frequency-Inverse Document Frequency (TF-IDF) with Logistic Regression (LR) achieved the best classification results.

Keywords:
feature extractionfunctional requirementsmachine learningnon-functional requirementssupport vector machinestext normalization

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Area of Science:

  • Software Engineering
  • Natural Language Processing
  • Machine Learning

Background:

  • Accurate software requirements classification is crucial in software engineering.
  • Existing methods require comparison of various feature extraction and machine learning techniques.

Purpose of the Study:

  • To compare Bag of Words (BoW), TF-IDF, and Chi Squared (CHI2) for feature extraction.
  • To evaluate Logistic Regression (LR), SVM, MNB, and kNN for requirements classification.
  • To determine the optimal combination for classifying functional and non-functional requirements.

Main Methods:

  • Utilized the PROMISE_exp dataset of labeled software requirements.
  • Applied text normalization, BoW, TF-IDF, and CHI2 for feature extraction/selection.
  • Implemented LR, SVM, MNB, and kNN for classification tasks.

Main Results:

  • TF-IDF with LR yielded the best F-measure (0.91 for binary classification, 0.74 for NF classification, 0.78 for general classification).
  • TF-IDF and LR combination outperformed other tested methods.
  • SVM tied with LR in binary classification performance.

Conclusions:

  • TF-IDF feature extraction combined with Logistic Regression is highly effective for software requirements classification.
  • The study provides a reproducible benchmark for future research in requirements engineering.
  • Further research will explore additional algorithms and precision improvement techniques.