Assessing the Effect of Imbalanced Learning on Cross-project Software Defect Prediction

Background: Software defect prediction models aim at identifying the potential faulty modules of a software project based on historical data collected from previous versions of the same project. Due to the lack of availability of software engineering data from the same project, the researchers proposed crossproject defect prediction (CPDP) models where the data collected from one or more projects are used to predict faults in other project. There are a number of approaches proposed with different levels of success and very limited repeatability. Goals: The purpose of this paper is to investigate the existing studies of cross-project models for defect prediction. It synthesizes the literature focusing on characteristics such as project type, software metrics, data preprocessing techniques, features selection approaches, classifiers, and performance measures used. Method: This paper follows the well-known Systematic Literature Review (SLR) approach proposed by Barbara Kitchenham in 2007. Results: Our finding shows that most of the article was published between 2015 and 2021. Moreover, the studies are mostly based on open-source datasets and the software metrics used to create the models are mainly product metrics. We also found out that most studies attempted to improve their models improving data preprocessing and feature selection approaches. Furthermore, logistic regression followed by naive bayes and random forest are the most adopted classifier techniques in such models. Finally, the f-measure followed by recall and AUC are the most preferred evaluation measure used to evaluate the performance of the models. Conclusions: This study provides an overview of the different approaches used to improve the CPDP models analyzing the different techniques used for data preprocessing, feature selection, and the selection of the classifiers. Moreover, we identified some aspects that need further investigation.

show abstract

“…• Artificial Neural Network evolved to Multilayer Perceptron [86] and Deep Adaptation Networks [112] • Boosting evolved into Ada Boost [106]…”

Section: ) Comparison Among Classifiersmentioning

confidence: 99%

Cross-Project Defect Prediction: A Literature Review

Pal

Sillitti²

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For a balanced learning model with an unbalanced test dataset, only the AUC value (area under the curve) increases exponentially. X. Cai et al in [21] proposed a hybrid multi-purpose dynamic local search Cuckoo Search (HMOCS) to simultaneously identify health solutions. The problem of class mismatch in the dataset and the selection of SVM (support vector machine) parameters is critical to the prediction software defect.…”

Section: A Software Defect Predictionmentioning

confidence: 99%

A New Learning to Rank Approach for Software Defect Prediction

Al-omari¹,

Elsheikh²,

Azzeh³

2022

IJACSA

View full text Add to dashboard Cite

Software defect prediction is one of the most active research fields in software development. The outcome of defect prediction models provides a list of the most likely defect-prone modules that need a huge effort from quality assurance teams. It can also help project managers to effectively allocate limited resources to validating software products and invest more effort in defect-prone modules. As the size of software projects grows, error prediction models can play an important role in assisting developers and shortening the time it takes to create more reliable software products by ranking software modules based on their defects. Therefore, there is need a learning-to-rank approach that can prioritize and rank defective modules to reduce testing effort, cost, and time. In this paper, a new learning to rank approach was developed to help the QA team rank the most defectprone modules using different regression models. The proposed approach was evaluated on a set of standardized datasets using well-known evaluation measures such as Fault-Percentile Average (FPA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Cumulative Lift Chart (CLC). Also, our proposed approach was compared with some other regression models that are used for software defect prediction, such as Random Forest (RF), Logistic Regression (LR), Support Vector Regression (SVR), Zero Inflated Regression (ZIR), Zero Inflated Poisson (ZIP), and Negative Polynomial Regression (NPR). Based on the results, the measurement criteria were different than each other as there was a gap in the accuracy obtained for defects prediction due to the nature of the random data, and thus was higher for RF and SVR, as well as FPA achieved better results than MAE and RMSE in this research paper.

show abstract

“…Finally, it uses CLNI filtering technique to clean the noise instances. Sohan et al [35], assessed the imbalance learning effect on CPDP by using eight different classifiers. Q.…”

Section: Related Workmentioning

confidence: 99%

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

2020

View full text Add to dashboard Cite

Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SMOTE (synthetic minority oversampling technique), a built-in package of many machine learning tools that is considered a benchmark in handling class imbalance problems, and with K-Means SMOTE. We conducted the experiment on forty open source software defect datasets from PRedict or Models in Software Engineering (PROMISE) repository using eight different classifiers and evaluated with six performance measures. The results show that the proposed CIR method shows improved performance over SMOTE and K-Means SMOTE.

show abstract

Assessing the Effect of Imbalanced Learning on Cross-project Software Defect Prediction

Cited by 11 publications

References 26 publications

Cross-Project Defect Prediction: A Literature Review

Cross-Project Defect Prediction: A Literature Review

A New Learning to Rank Approach for Software Defect Prediction

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

Contact Info

Product

Resources

About