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Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies
Aakanshi Gupta1, Bharti Suri2, Vijay Kumar3
1Department of Computer Science and Engineering, Amity School of Engineering and Technology, New Delhi 110061, India.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces a novel mathematical model using information theory entropy to predict software bad smells in open-source projects like Apache Abdera. The model helps anticipate future code quality issues, aiding software development industries.
Area of Science:
- Software Engineering
- Information Theory
- Predictive Modeling
Background:
- High-quality software development requires managing code modifications that can introduce "bad smells," degrading reliability.
- Open-source software, like Apache Abdera, is frequently modified, increasing the risk of these code defects.
- Existing methods for identifying bad smells may not adequately predict future occurrences.
Purpose of the Study:
- To propose a mathematical model for predicting software bad smells.
- To leverage information theory entropy measures for this prediction.
- To validate the model's accuracy and applicability in real-world open-source projects.
Main Methods:
- Developed a predictive model based on information theory entropy (Shannon, Rényi, Tsallis).
- Collected bad smell data from Apache Abdera using a detection tool.
- Applied non-linear regression techniques to predict future bad smells based on historical data and entropy measures.
- Validated the model using goodness-of-fit parameters and standard statistical metrics (R², MSE, RMSPE).
Main Results:
- The proposed model accurately predicts future software bad smells based on entropy measures.
- Validation metrics (prediction error, bias, variation, RMSPE, R², adjusted R², MSE) confirm the model's reliability.
- Comparison with observed data shows the model's effectiveness in real-world scenarios.
Conclusions:
- The entropy-based mathematical model offers a reliable method for predicting software bad smells.
- This predictive capability can significantly benefit software development industries in maintaining code quality proactively.
- The findings provide valuable insights for future research in software defect prediction and maintenance.
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