PROMISE Repository Research Articles

A hybrid‐ensemble model for software defect prediction for balanced and imbalanced datasets using AI‐based techniques with feature preservation: SMERKP‐XGB

AbstractMaintaining software quality is a significant challenge as the complexity of software is increasing with the rise of the software industry. Software defects are a primary concern in complex modules, and predicting them in the early stages of the software development life cycle (SDLC) is difficult. Previous techniques to address this issue have not been very promising. We have proposed “A hybrid ensemble model for software defect prediction using AI‐based techniques with feature preservation” to overcome this problem. We have used the National Aeronautics and Space Administration (NASA) dataset from the PROMISE repository for testing and validation. By applying exploratory data analysis (EDA), feature engineering, scaling, and standardization, we found that the dataset is imbalanced, which can negatively affect the model's performance. To address this, we have used the Synthetic Minority Oversampling (SMOTE) technique and the edited nearest neighbor (ENN) (SMOTE‐ENN). We have also used recursive feature elimination cross‐validation (RFE‐CV) with a pipeline to prevent data leaking in CV and kernel‐based principal component analysis (K‐PCA) to minimize dimensionality and selectively relevant features. The reduced dimensional data is then given to the eXtreme Gradient Boosting (XGBoost) for classification, resulting in the hybrid‐ensemble (SMERKP‐XGB) model. The proposed SMERKP‐XGB model is better than previously developed models in terms of accuracy (CM1: 97.53%, PC1: 92.05%, and PC2: 97.45%, KC1:95.65%), and area under the receiver operating characteristic curve values (CM1:96.30%, PC1:98.30%, and PC2:99.30%: KC1: 93.54) and other evaluation criteria mentioned in the literature.

Bioprospecting of Aspergillus sp. as a promising repository for anti-cancer agents: a comprehensive bibliometric investigation.

Cancer remains a significant global health challenge, claiming nearly 10 million lives in 2020 according to the World Health Organization. In the quest for novel treatments, fungi, especially Aspergillus species, have emerged as a valuable source of bioactive compounds with promising anticancer properties. This study conducts a comprehensive bibliometric analysis to map the research landscape of Aspergillus in oncology, examining publications from 1982 to the present. We observed a marked increase in research activity starting in 2000, with a notable peak from 2005 onwards. The analysis identifies key contributors, including Mohamed GG, who has authored 15 papers with 322 citations, and El-Sayed Asa, with 14 papers and 264 citations. Leading countries in this research field include India, Egypt, and China, with King Saud University and Cairo University as the leading institutions. Prominent research themes identified are "endophyte," "green synthesis," "antimicrobial," "anti-cancer," and "biological activities," indicating a shift towards environmentally sustainable drug development. Our findings highlight the considerable potential of Aspergillus for developing new anticancer therapies and underscore the necessity for further research to harness these natural compounds for clinical use.

Predicting the Number of Software Faults using Deep Learning

The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.

Cross-Project Defect Prediction Using Transfer Learning with Long Short-Term Memory Networks

With the increasing number of software projects, within-project defect prediction (WPDP) has already been unable to meet the demand, and cross-project defect prediction (CPDP) is playing an increasingly significant role in the area of software engineering. The classic CPDP methods mainly concentrated on applying metric features to predict defects. However, these approaches failed to consider the rich semantic information, which usually contains the relationship between software defects and context. Since traditional methods are unable to exploit this characteristic, their performance is often unsatisfactory. In this paper, a transfer long short-term memory (TLSTM) network model is first proposed. Transfer semantic features are extracted by adding a transfer learning algorithm to the long short-term memory (LSTM) network. Then, the traditional metric features and semantic features are combined for CPDP. First, the abstract syntax trees (AST) are generated based on the source codes. Second, the AST node contents are converted into integer vectors as inputs to the TLSTM model. Then, the semantic features of the program can be extracted by TLSTM. On the other hand, transferable metric features are extracted by transfer component analysis (TCA). Finally, the semantic features and metric features are combined and input into the logical regression (LR) classifier for training. The presented TLSTM model performs better on the f-measure indicator than other machine and deep learning models, according to the outcomes of several open-source projects of the PROMISE repository. The TLSTM model built with a single feature achieves 0.7% and 2.1% improvement on Log4j-1.2 and Xalan-2.7, respectively. When using combined features to train the prediction model, we call this model a transfer long short-term memory for defect prediction (DPTLSTM). DPTLSTM achieves a 2.9% and 5% improvement on Synapse-1.2 and Xerces-1.4.4, respectively. Both prove the superiority of the proposed model on the CPDP task. This is because LSTM capture long-term dependencies in sequence data and extract features that contain source code structure and context information. It can be concluded that: (1) the TLSTM model has the advantage of preserving information, which can better retain the semantic features related to software defects; (2) compared with the CPDP model trained with traditional metric features, the performance of the model can validly enhance by combining semantic features and metric features.

A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI.

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC.

Anticancer, Immunomodulatory, and Phytochemical Screening of Carthamus oxyacantha M.Bieb Growing in the North of Iraq.

Carthamus oxyacantha M.Bieb is a promising repository of active phytochemicals. These bioactive compounds work synergistically to promote the plant's antioxidant, anticancer, and immunomodulatory capabilities. The present study aimed to discover the potential immunomodulatory and cytotoxicity of different extracts of Carthamus oxycantha roots. Aqueous ethanol (70%), aqueous methanol (90%), ethyl acetate, and n-hexane extracts were tested against five cell lines (T47D, MDA-MB231, Caco-2, EMT6/P, and Vero). Among these extracts, ethyl acetate and n-hexane extracts showed significant activity in inhibiting the proliferation of cancerous cells because of the presence of several phytochemical compounds, including flavonoids, phenolics, and alkaloids. The n-hexane extract was the most potent extract against T47D and Caco-2 cell lines and had IC50 values of 0.067 mg/mL and 0.067 mg/mL, respectively. In comparison, ethyl acetate extract was active against T47D and MDAMB231, and IC50 values were 0.0179 mg/mL and 0.03 mg/mL, respectively. Both n-hexane and ethyl acetate extracts reduced tumor size (by 49.981% and 51.028%, respectively). Remarkably, Carthamus oxyacantha extracts decreased the average weight of the tumor cells in the in vivo model. The plant induced significant apoptotic activity by the activation of caspase-3, immunomodulation of macrophages, and triggering of pinocytosis. The implications of these intriguing findings demand additional research to broaden the scope of the understanding of this field, opening the doors to the possibilities of using Carthamus oxyacantha M.Bieb as an effective cancer treatment adjuvant in the future.

Ensemble learning based software defect prediction

Currently, the cost to detect and solve software defects is a heavy burden on software projects. So, it is significant to predict software defects at the earlier stages of the software development lifecycle. In this study, seven commonly-used machine learning and deep learning algorithms were studied and the performance of defect classification on 4 representative public datasets from NASA and the PROMISE repository was demonstrated. Furthermore, three classical ensemble learning methods (Bagging, Boosting, and Stacking) were used to improve the prediction performance. Six metrics, including accuracy, precision, f1-score, recall, the area under the receiver operating characteristic curve (AUC), and G-Mean were utilized to evaluate the performance. It was noted that ensemble learning exceeded all the other seven algorithms. Ensemble learning achieved the highest AUC of 0.99, the highest G-Mean of 0.96, and an average F1-score of 0.97. Under a time-sensitive scenario, the Boosting method was a good choice as it spent less runtime and had a similar performance to the other two ensemble learning methods in most cases.

Software defect prediction using a bidirectional LSTM network combined with oversampling techniques

AbstractSoftware defects are a critical issue in software development that can lead to system failures and cause significant financial losses. Predicting software defects is a vital aspect of ensuring software quality. This can significantly impact both saving time and reducing the overall cost of software testing. During the software defect prediction (SDP) process, automated tools attempt to predict defects in the source codes based on software metrics. Several SDP models have been proposed to identify and prevent defects before they occur. In recent years, recurrent neural network (RNN) techniques have gained attention for their ability to handle sequential data and learn complex patterns. Still, these techniques are not always suitable for predicting software defects due to the problem of imbalanced data. To deal with this problem, this study aims to combine a bidirectional long short-term memory (Bi-LSTM) network with oversampling techniques. To establish the effectiveness and efficiency of the proposed model, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR) and mean square error (MSE). The average accuracy of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) was 88%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average accuracy by 6 and 4% compared to the original datasets. The average F-measure of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) were 51%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average F-measure by 43 and 41% compared to the original datasets. The experimental results demonstrated that combining the Bi-LSTM network with oversampling techniques positively affects defect prediction performance in datasets with imbalanced class distributions.

Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

AbstractCross‐version defect prediction (CVDP) for evolutionary projects has attracted much attention from researchers in recent years. For multiple versions of an object‐oriented project, the degree of evolution (e.g., the degree of class change) between successive versions can reflect the differences between versions, which could affect the performance of CVDP. Therefore, how to measure the degree of evolution between successive versions and explore the correlations with the performance of CVDP are very important for software defect prediction. Based on the successive versions of evolutionary projects, this paper proposes six evolutionary measures from three aspects of class change, metric change, and label change, including the Ratio of New Classes (RNC), the Ratio of Deleted Classes (RDC), the Average Ratio of Metric Change (ARMC), the Ratio of Label Changed Classes (RLCC), the Ratio of Unchanged Classes (RUC), and the Ratio of Interference Classes (RIC). An empirical study was conducted on 40 versions of 11 object‐oriented projects from the PROMISE repository. Precision, Recall, F‐measure, and AUC were used as the performance indicators. Three correlation approaches (Pearson, Spearman, and Kendall) are applied to show the correlations between evolutionary measures and the performance of CVDP. The statistical results show that RNC, RDC, and RUC show no correlation with four performance indicators. ARMC shows weak or medium positive correlations with Recall and F‐measure. RLCC and RIC show very strong or strong negative correlations with Recall and F‐measure. The results indicate that the correlations between the proposed evolutionary measures and the performance of CVDP are different, which can guide the training set selection of CVDP.

A Novel Developed Supervised Machine Learning System For Classification And Prediction of Software Faults Using NASA Dataset

The software systems of modern computers are extremely complex and versatile. Therefore, it is essential to regularly detect and correct software design faults. In order to devote resources effectively towards the creation of trustworthy software, software companies are increasingly engaging in the practise of predicting fault-prone modules in advance of testing. These software fault prediction methods rely on the thoroughness with which prior software versions' fault as well as related code has been retrievedTime, energy, and money are all saved as a result. Increases the company's initial success and bottom line greatly by satisfying its clientele. Numerous academics have poured into this area throughout the years in an effort to raise the bar for all software. Nowadays, The most often used approaches in this field are those based on machine learning (ML). The field of ML seeks to perfect software capable of evolving as well as adapting in response to fresh data. This paper introduces a fresh approach for doing ML by bringing together a number of different expert systems. In order to reach agreement on which aspects of a software system need to be tested, the proposed multi-classifier model pools the strengths of the most effective classifiers. Several top-performing classifiers for defect prediction are put through their paces in an experiential evaluation. We test our method on 16 publicly available datasets from the NASA Metric Data Programme (MDP) repository at the promise repository. Parameters of confusion, recall, precision, recognition accuracy, etc., are evaluated and contrasted with existing schemes in a software analysis performed with the help of the python simulation tool with findings. The experimental outcomes demonstrate that by combining LGBM, XGBoost, and Voting classifiers, using a multi classifier approach, we are capable to significantly improve software fault prediction performance. The results of the investigation show that the suggested method will lead to better practical outcomes in the prediction of device failures.

Software Testing: A Prediction Techniques

Prediction of software defects using machine learning techniques has attracted more attention from researchers due to its importance in producing successful software. On the other side, it reduces the cost of software development and facilitates procedures to identify the reasons for determining the percentage of defect-prone software in the future. There is no conclusive evidence for specific types of machine learning that will be more efficient and accurate in predicting software defects. However, some of the previous related work proposes ensemble learning techniques as a more accurate alternative. The paper presents the resample technique with three types of ensemble learners; Boosting, Bagging, and Rotation Forest, using eight base learners tested on seven types of benchmark datasets provided in the PROMISE repository. Results indicate that accuracy has been improved using ensemble techniques more than single leaners especially in conjunction with Rotation Forest with the resample technique in most of the algorithms used in the experimental results.

A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method

Software defect prediction (SDP) plays a vital role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to using historical defect data to construct a relationship between software metrics and defects via diverse methodologies. Several prediction models, such as machine learning (ML) and deep learning (DL), have been developed and adopted to recognize software module defects, and many methodologies and frameworks have been presented. Class imbalance is one of the most challenging problems these models face in binary classification. However, When the distribution of classes is imbalanced, the accuracy may be high, but the models cannot recognize data instances in the minority class, leading to weak classifications. So far, little research has been done in the previous studies that address the problem of class imbalance in SDP. In this study, the data sampling method is introduced to address the class imbalance problem and improve the performance of ML models in SDP. The proposed approach is based on a convolutional neural network (CNN) and gated recurrent unit (GRU) combined with a synthetic minority oversampling technique plus the Tomek link (SMOTE Tomek) to predict software defects. To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, F-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), and mean square error (MSE). The experimental results showed that the proposed models predict the software defects more effectively on the balanced datasets than the original datasets, with an improvement of up to 19% for the CNN model and 24% for the GRU model in terms of AUC. We compared our proposed approach with existing SDP approaches based on several standard performance measures. The comparison results demonstrated that the proposed approach significantly outperforms existing state-of-the-art SDP approaches on most datasets.

Machine learning based Software Fault Prediction models

The study aims to identify soft-computing-based software fault prediction models that assist in resolving issues related to the quality, reliability, and cost of the software projects. It proposes models for implementation of software fault prediction using decision-tree regression and the K-nearest neighbor technique of machine learning. The proposed models have been designed and implemented in Python using designed metric suites as input, and the predicted-faults as output, for the real-time, wider dataset from the Promise repository. By comparing the prediction and validation results of the proposed models for the same dataset, it has been concluded that the decision-tree regression-based fault prediction model has the best performance with values of MMRE, RMSE, and accuracy of 0.0000204, 3.54, and 99.37, respectively.

Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning.

Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

Performance Evaluation of Convolutional Neural Network for Multi-Class in Cross Project Defect Prediction

Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification of the source data, removing the noise, reducing the distribution gap, and balancing the output classes—are an ongoing challenge, as is the selection of an optimal feature set. This research paper aims to achieve a higher defect prediction accuracy for multi-class CPDP by selecting an optimal feature set through XGBoost combined with an automatic feature extraction using a convolutional neural network (CNN). This research type is explanatory, and this research method is controlled experimentation, for which the independent variable prediction accuracy was dependent upon two variables, XGBoost and CNN. The Softmax layer was added to the output layers of the CNN classifier to classify the output into multiple classes. In our experimentation with CPDP, we selected all 28 versions of the multi-class, in which 11 versions were selected as the source projects, against which we predicted 28 target versions with an average AUC of 75.57%. We validated this research paper’s results through the Wilcoxon test. Therefore, after removing the noise, class imbalances, and the data distribution gap, and treating the PROMISE dataset as multi-class, the optimal features selected through XGBoost and classified through the CNN can substantially increase the prediction accuracy in CPDP as evident from our exploratory data analysis (EDA).

Design of a Hybrid Machine Learning Base-Classifiers for Software Defect Prediction

Machine learning (ML) classifiers have attracted the research community's attention in the field of software defect prediction (SDP) in the last decade. Several comparative studies confirmed that algorithms like Naí¯ve Bayes, Decision tree, or Random Forest have been used with good performance. Recently, several hybrid classifiers were also introduced in SDP. However, many of those can only provide a category for a given new sample. Instead, this paper proposes a methodology to build a hybrid of four (4) ML Base-classifiers made up of Gaussian Naive Bayes, Bernoulli Naive Bayes, Random Forest, and support vector machine (SVM). The study carefully ensemble the selected machine learning models with efficient feature selection to address these issues and mitigate their effects on the defect prediction performance using dataset CM1 from PROMISE repository. The study outcome is expected to show promising SDP performance in terms of F1-score compared to the benchmark work.

A cognitive and neural network approach for software defect prediction

Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments.

An Approach to Software Defect Prediction Combining Semantic Features and Code Changes

Software defect prediction (SDP), which predicts defective code regions, can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts. Previous work on defect prediction has used machine learning and artificial software metrics. However, traditional defect prediction features extracted from artificial software metrics often fail to capture the syntactic and semantic information of defective modules. This work on defect prediction mostly focuses on abstract syntax tree (AST). Moreover, because current research on AST technology is relatively mature, it is difficult to further improve the accuracy of defect prediction when only using AST to characterize codes. In this paper, in order to capture more semantic features, we extract semantic information both from the sequences of AST tokens and code change tokens. In addition, to leverage the traditional features extracted from statistical metrics, we also combine the semantic features with traditional defect prediction features to perform SDP, and use the gated fusion mechanism to determine the combination ratio of the two kinds of features. In our empirical studies, 10 open-source Java projects from the PROMISE repository are chosen as our empirical subjects. Experimental results show that our proposed approach can perform better than several state-of-the-art baseline SDP methods.

Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction

<p>Cross-project defect prediction (CPDP) is a field of study that allows predicting defects in software projects for which the availability of data is limited and produces generalizable prediction models. Due to the heterogeneity of cross projects, CPDP is particularly challenging and several methods have been employed to address this problem. Nevertheless, the class-imbalanced characteristic of the cross-project defect data also increases the learning difficulty of such a task but has not been investigated in depth. This paper proposed a novel, cost-cognitive ensemble method for CPDP, which includes four phases: bagging balanced resampling phase, base classifiers learning phase, cost value cognitive phase, and base classifiers ensemble phase. These phases create a composition of classifiers that are used for predicting defects. Results of an empirical evaluation on 10 datasets from the PROMISE repository indicated that our method achieves the best overall performance with respect to conventional methods. Moreover, our method could cognize the cost value automatically during the model training, it is shown to be more effective and practical.</p> <p> </p>

Feature Clustering and Ensemble Learning Based Approach for Software Defect Prediction

Objective:: Defects in delivered software products not only have financial implications but also blemish the reputation of the organisation and lead to wastage of time and human resource. This paper aims to detect defects in software modules. Methods:: Our approach sequentially combines SMOTE algorithm to deal with class imbalance problem, K - means clustering algorithm to obtain a set of key features based on inter-class and intra-class coefficient of correlation and ensemble modelling to predict defects in software modules. After cautious examination, an ensemble framework of XGBoost, Decision Tree and Random Forest is used for prediction of software defects owing to numerous merits of ensembling approach. Results:: We have used five open-source datasets from NASA Promise Repository for Software Engineering. The result obtained from our approach has been compared with that of individual algorithms used in ensemble. A confidence interval for the accuracy of our approach with respect to performance evaluation metrics namely Accuracy, Precision, Recall, F1 score and AUC score has also been constructed at a significance level of 0.01. Conclusion:: Results have been depicted pictographically.