scholarly journals A New Framework Consisted of Data Preprocessing and Classifier Modelling for Software Defect Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Haijin Ji ◽  
Song Huang
Keyword(s):  
Software Metrics ◽  
Data Preprocessing ◽  
Defect Prediction ◽  
Data Repository ◽  
Software Defect Prediction ◽  
Software Projects ◽  
Software Defect ◽  
Nonnormal Distribution ◽  
Non Gaussian ◽  
New Framework

Different data preprocessing methods and classifiers have been established and evaluated earlier for the software defect prediction (SDP) across projects. These novel approaches have provided relatively acceptable prediction results for different software projects. However, to the best of our knowledge, few researchers have combined data preprocessing and building robust classifier simultaneously to improve prediction performances in SDP. Therefore, this paper presents a new whole framework for predicting fault-prone software modules. The proposed framework consists of instance filtering, feature selection, instance reduction, and establishing a new classifier. Additionally, we find that the 21 main software metrics commonly do follow nonnormal distribution after performing a Kolmogorov-Smirnov test. Therefore, the newly proposed classifier is built on the maximum correntropy criterion (MCC). The MCC is well-known for its effectiveness in handling non-Gaussian noise. To evaluate the new framework, the experimental study is designed with due care using nine open-source software projects with their 32 releases, obtained from the PROMISE data repository. The prediction accuracy is evaluated using F-measure. The state-of-the-art methods for Cross-Project Defect Prediction are also included for comparison. All of the evidences derived from the experimentation verify the effectiveness and robustness of our new framework.

Software Defect Prediction Using Genetic Programming and Neural Networks

2020 ◽  
pp. 1577-1597
Author(s):  
Mohammed Akour ◽  
Wasen Yahya Melhem
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Detailed Analysis ◽  
Software Quality ◽  
The Other ◽  
Defect Prediction ◽  
Data Repository ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Software Defect

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.

scholarly journals Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

2019 ◽  
Vol 9 (13) ◽  
pp. 2764 ◽  
Author(s):  
Abdullateef Oluwagbemiga Balogun ◽  
Shuib Basri ◽  
Said Jadid Abdulkadir ◽  
Ahmad Sobri Hashim
Keyword(s):  
Software Metrics ◽  
Prediction Models ◽  
Predictive Performance ◽  
Search Method ◽  
Feature Subset Selection ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect

Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models, and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance.

The Use of Ensemble-Based Data Preprocessing Techniques for Software Defect Prediction

2014 ◽  
Vol 24 (09) ◽  
pp. 1229-1253 ◽  
Author(s):  
Kehan Gao ◽  
Taghi M. Khoshgoftaar ◽  
Amri Napolitano
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Measurement Data ◽  
Class Imbalance ◽  
Data Preprocessing ◽  
High Dimensionality ◽  
Training Dataset ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect

Software defect prediction models that use software metrics such as code-level measurements and defect data to build classification models are useful tools for identifying potentially-problematic program modules. Effectiveness of detecting such modules is affected by the software measurements used, making data preprocessing an important step during software quality prediction. Generally, there are two problems affecting software measurement data: high dimensionality (where a training dataset has an extremely large number of independent attributes, or features) and class imbalance (where a training dataset has one class with relatively many more members than the other class). In this paper, we present a novel form of ensemble learning based on boosting that incorporates data sampling to alleviate class imbalance and feature (software metric) selection to address high dimensionality. As we adopt two different sampling methods (Random Undersampling (RUS) and Synthetic Minority Oversampling (SMOTE)) in the technique, we have two forms of our new ensemble-based approach: selectRUSBoost and selectSMOTEBoost. To evaluate the effectiveness of these new techniques, we apply them to two groups of datasets from two real-world software systems. In the experiments, four learners and nine feature selection techniques are employed to build our models. We also consider versions of the technique which do not incorporate feature selection, and compare all four techniques (the two different ensemble-based approaches which utilize feature selection and the two versions which use sampling only). The experimental results demonstrate that selectRUSBoost is generally more effective in improving defect prediction performance than selectSMOTEBoost, and that the techniques with feature selection do help for getting better prediction than the techniques without feature selection.

Combining Data Preprocessing Methods With Imputation Techniques for Software Defect Prediction

2018 ◽  
Vol 9 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Misha Kakkar ◽  
Sarika Jain ◽  
Abhay Bansal ◽  
P.S. Grover
Keyword(s):  
Linear Regression ◽  
Missing Values ◽  
Data Preprocessing ◽  
Machine Learning Algorithms ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Combining Data ◽  
Feature Selector ◽  
Traditional Performance

Software Defect Prediction (SDP) models are used to predict, whether software is clean or buggy using the historical data collected from various software repositories. The data collected from such repositories may contain some missing values. In order to estimate missing values, imputation techniques are used, which utilizes the complete observed values in the dataset. The objective of this study is to identify the best-suited imputation technique for handling missing values in SDP dataset. In addition to identifying the imputation technique, the authors have investigated for the most appropriate combination of imputation technique and data preprocessing method for building SDP model. In this study, four combinations of imputation technique and data preprocessing methods are examined using the improved NASA datasets. These combinations are used along with five different machine-learning algorithms to develop models. The performance of these SDP models are then compared using traditional performance indicators. Experiment results show that among different imputation techniques, linear regression gives the most accurate imputed value. The combination of linear regression with correlation based feature selector outperforms all other combinations. To validate the significance of data preprocessing methods with imputation the findings are applied to open source projects. It was concluded that the result is in consistency with the above conclusion.

Combining Data Preprocessing Methods With Imputation Techniques for Software Defect Prediction

2021 ◽  
pp. 1792-1811
Author(s):  
Misha Kakkar ◽  
Sarika Jain ◽  
Abhay Bansal ◽  
P.S. Grover
Keyword(s):  
Linear Regression ◽  
Missing Values ◽  
Data Preprocessing ◽  
Machine Learning Algorithms ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Combining Data ◽  
Feature Selector ◽  
Traditional Performance

Software Defect Prediction (SDP) models are used to predict, whether software is clean or buggy using the historical data collected from various software repositories. The data collected from such repositories may contain some missing values. In order to estimate missing values, imputation techniques are used, which utilizes the complete observed values in the dataset. The objective of this study is to identify the best-suited imputation technique for handling missing values in SDP dataset. In addition to identifying the imputation technique, the authors have investigated for the most appropriate combination of imputation technique and data preprocessing method for building SDP model. In this study, four combinations of imputation technique and data preprocessing methods are examined using the improved NASA datasets. These combinations are used along with five different machine-learning algorithms to develop models. The performance of these SDP models are then compared using traditional performance indicators. Experiment results show that among different imputation techniques, linear regression gives the most accurate imputed value. The combination of linear regression with correlation based feature selector outperforms all other combinations. To validate the significance of data preprocessing methods with imputation the findings are applied to open source projects. It was concluded that the result is in consistency with the above conclusion.

A New Data Mining-Based Framework to Test Case Prioritization Using Software Defect Prediction

2017 ◽  
Vol 8 (1) ◽  
pp. 21-41
Author(s):  
Emad Alsukhni ◽  
Ahmad A. Saifan ◽  
Hanadi Alawneh
Keyword(s):  
Data Mining ◽  
Software Metrics ◽  
Fault Prediction ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Software Defect Prediction ◽  
Average Percentage ◽  
Software Defect ◽  
New Framework

Test cases do not have the same importance when used to detect faults in software; therefore, it is more efficient to test the system with the test cases that have the ability to detect the faults. This research proposes a new framework that combines data mining techniques to prioritize the test cases. It enhances fault prediction and detection using two different techniques: 1) the data mining regression classifier that depends on software metrics to predict defective modules, and 2) the k-means clustering technique that is used to select and prioritize test cases to identify the fault early. Our approach of test case prioritization yields good results in comparison with other studies. The authors used the Average Percentage of Faults Detection (APFD) metric to evaluate the proposed framework, which results in 19.9% for all system modules and 25.7% for defective ones. Our results give us an indication that it is effective to start the testing process with the most defective modules instead of testing all modules arbitrary arbitrarily.

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