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Entropy02:39

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
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The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the...
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Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies.

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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.

Keywords:
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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.