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Measuring Software Maintainability with Naïve Bayes Classifier.

Nayyar Iqbal1, Jun Sang1, Jing Chen1

  • 1School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China.

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Summary
This summary is machine-generated.

This study introduces a supervised learning method to assess legacy software components for maintainability. The approach accurately identifies necessary changes, improving software evolution and reducing technical debt.

Keywords:
Naïve BayesWEKA softwareerrorssoftware componentssoftware requirementssupervised learning

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Computer Science

Background:

  • Software systems evolve due to changing business needs, technology, and customer requirements.
  • Maintaining legacy systems presents significant challenges for software companies.
  • Assessing software maintainability is crucial for effective system evolution.

Purpose of the Study:

  • To develop and evaluate a supervised learning approach for identifying necessary changes in legacy software components.
  • To assess the maintainability of legacy systems by analyzing various factors like quality, business value, and error types.
  • To measure software maintainability using a machine learning technique, specifically the Naïve Bayes classifier.

Main Methods:

  • A system assessment was conducted from diverse perspectives including quality, business value, and error types.
  • Supervised learning was employed to identify required modifications in existing software components of legacy systems.
  • New interfaces were redesigned based on identified requirements and error types.
  • The Naïve Bayes classifier was applied to measure software maintainability using a dataset of component attributes.

Main Results:

  • The methodology successfully identified changes required for legacy software components.
  • New interfaces were redesigned to meet new requirements and address identified errors.
  • The Naïve Bayes classifier achieved a high accuracy of 97.18% in measuring software maintainability.
  • The Waikato Environment for Knowledge Analysis (WEKA) software validated the effectiveness of the proposed methodology.

Conclusions:

  • The introduced supervised learning methodology is effective for assessing and improving the maintainability of legacy software systems.
  • Machine learning, particularly the Naïve Bayes classifier, provides an accurate means to measure software maintainability.
  • This approach aids in determining whether reverse or forward engineering is more suitable for software maintenance tasks.