Evaluation of sampling techniques in software fault prediction using metrics and code smells

Kaur, Kamaldeep; Kaur, Parmeet

doi:10.1109/icacci.2017.8126033

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…Kendall rank correlation τ: Kendall rank correlation 27 is a nonparametric test that measures the strength of dependence between two variables. As shown in Equation (6). we consider two samples, a and b, where each sample size is n, and we know that the total number of pairings with a b is n (n À 1) /2.…”

Section: Multinomial Logistic Regressionmentioning

confidence: 99%

“…The main problem with imbalanced classification is that it has a small number of training examples of the minority class for a model efficacious learn the decision boundary. SMOTE 6 has been applied by duplicate number of examples from the minority class in the training dataset before fitting a training model. SMOTE generate the training examples by using linear interpolation techniques, and it selects samples of feature space for each target class and its closest neighbors.…”

Section: The Proposed Learning Modelmentioning

confidence: 99%

“…Therefore, several code smells detectors have been proposed recently to automatically detect code smells in source code 4,5 . The types of code smells detectors differ according to the main algorithm used, for example, search‐based techniques 6 versus metric‐based techniques 7 and the metric‐based techniques are classified into process metrics 8 and product metrics.…”

Section: Introductionmentioning

confidence: 99%

“…according to the main algorithm used, for example, search-based techniques 6 versus metric-based techniques 7 and the metric-based techniques are classified into process metrics 8 and product metrics.…”

mentioning

confidence: 99%

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Severity classification of software code smells using machine learning techniques: A comparative study

Abdou

Darwish

2022

J Software Evolu Process

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Code smell is a software characteristic that indicates bad symptoms in code design which causes problems related to software quality. The severity of code smells must be measured because it will help the developers when determining the priority of refactoring efforts. Recently, several studies focused on the prediction of design patterns errors using different detection tools. Nowadays, there is a lack of empirical studies regarding how to measure severity of code smells and which learning model is best to detect the severity of code smells. To overcome such gap, this paper focuses on measuring the severity classification of code smells depending on several machine learning models such as regression models, multinominal models, and ordinal classification models. The Local Interpretable Model Agnostic Explanations (LIME) algorithm was further used to explain the machine learning model's predictions and interpretability. On the other side, we extract the prediction rules generated by the Projective Adaptive Resonance Theory (PART) algorithm in order to study the effectiveness of using software metrics to predict code smells. The results of the experiments have shown that the accuracy of severity classification model is enhanced than baseline and ranking correlation between the predicted and actual model reaches 0.92–0.97 by using Spearman's correlation measure.

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Section: Multinomial Logistic Regressionmentioning

confidence: 99%

Section: The Proposed Learning Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Severity classification of software code smells using machine learning techniques: A comparative study

Abdou

Darwish

2022

J Software Evolu Process

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“…However, the imbalanced distribution of software faults in source code leads to poor prediction power of machine learning techniques applied to predict source code defects such as bad smells [ 50 ]. Hassan [ 51 ] proposed the information theory concept to measure the amount of randomness or entropy of the distribution to quantify the code complexity as a result of code changes.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

Gupta¹,

Suri

Kumar³

et al. 2018

Entropy

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Abstract:The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (R 2 , adjusted R 2 , Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

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Software Defect Prediction Using Bad Code Smells: A Systematic Literature Review

Piotrowski¹,

Madeyski²

2019

Data-Centric Business and Applications

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Evaluation of sampling techniques in software fault prediction using metrics and code smells

Cited by 8 publications

References 13 publications

Severity classification of software code smells using machine learning techniques: A comparative study

Severity classification of software code smells using machine learning techniques: A comparative study

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

Software Defect Prediction Using Bad Code Smells: A Systematic Literature Review

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