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Related Experiment Video

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External Cephalic Version: Is it an Effective and Safe Procedure?
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Mining version history to predict the class instability.

Shahid Hussain1, Humaira Afzal2, Muhammad Rafiq Mufti3

  • 1Department of Computer Science, COMSATS University, Islamabad, Pakistan.

Plos One
|September 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Historical Information for Class Stability Prediction (HICSP) to identify instable software classes. HICSP improves prediction accuracy by analyzing historical change data, aiding developers in system maintenance.

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Area of Science:

  • Software Engineering
  • Empirical Software Engineering
  • Software Maintenance

Background:

  • Existing class stability assessments primarily use static structural information.
  • Class stability is influenced by evolving dependencies and change propagation, leading to ripple effects.
  • Identifying instable classes prone to ripple effects is crucial for efficient software evolution.

Purpose of the Study:

  • To propose Historical Information for Class Stability Prediction (HICSP), an approach leveraging historical data to predict instable classes.
  • To evaluate HICSP's effectiveness in identifying classes susceptible to the ripple effect.
  • To enhance software maintenance by providing developers with tools to predict and mitigate instability.

Main Methods:

  • Developed the Historical Information for Class Stability Prediction (HICSP) approach.
  • Utilized version history data and change propagation factors to identify instable classes.
  • Conducted two empirical studies on 10 open-source projects, including a replicated study with parameter tuning.

Main Results:

  • HICSP demonstrated improved prediction of instable classes compared to existing methods.
  • Observed a 4% to 16% improvement in F-measure for predicting instable classes.
  • Validated HICSP's effectiveness through empirical evaluations on diverse open-source projects.

Conclusions:

  • HICSP effectively predicts instable classes by analyzing historical change information.
  • The approach aids developers in making informed decisions for software maintenance and evolution.
  • Leveraging historical data significantly enhances the accuracy of class stability prediction.