A Framework for Software Defect Prediction Using Neural Networks

In recent years, data mining algorithm have been successfully applied in software defects and metrics prediction throughout the software development life cycle (SDLC). Features dependencies and class imbalance are the two main issues in the traditional Bayesian models for defect prediction. Traditional supervised models build high prediction rate with limited single product defect and metric data. Various models have been introduced in the literature to establish a common framework for defect and metric prediction in academia, standards and industry, but none of them are accepted as a common tool in real-time software products. Also, predicting the defects cum metrics in multiple products is one of the common problems in real time associated products. In this proposed model, a new ensemble defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns. Experimental results show that proposed model has high true positive rate compare to traditional Bayesian network models.

Software defects and quality assurance are crucial aspects of software development that should be considered during the software development cycle. To ensure high-quality software, it is essential to have a robust quality assurance process in place. System reliability and quality are very key components that must be considered during software development, and this can only be achieved when software undergoes a thorough test process for errors, anomalies, defects, omissions, and bugs. Early software defect prediction and detection play an essential role in ensuring the reliability and quality of software systems, ensuring that software companies discover errors or defects early enough and allocate more resources to defect-prone modules. This study proposes the development of an enhanced classifier model for software defect prediction and detection. The aim is to harness the collective intelligence of selected base classifiers like Support Vector Machine, Logistic regression, Decision Trees, Random Forest, AdaBoost, Gradient Boosting, K-Nearest Neighbor, GaussianNB, and Multi-Layer Perception to improve accuracy, robustness, and generalization in identifying potential defects using a soft voting ensemble technique. The ensemble model leveraged the confidence probability of the soft voting technique and the generalization advantage of cross-validation leading to a more robust and dynamic model. The performance of the model with existing classifiers was evaluated using accuracy, F1 score, Precision, and area under the ROC curve (ROC- AUC) as the evaluation metrics. The results of the experiment revealed that the Proposed Classifier produced an overall Accuracy rate of 93%, and ROC AUC of 98%. The results demonstrate the effectiveness of our enhanced ensemble classifier in software defect detection and prediction. By harnessing the strengths of diverse base classifiers, our approach provides a robust and adaptive solution to the challenges of early detection and mitigating defects in software systems. This research contributes to the advancement of reliable software development practices and lays the foundation for future enhancements in ensemble-based defect detection methodologies.

Software testing plays important role in software development life cycle (SDLC) while dealing with critical business applications. From SDLC 70% of time and cost is used for testing only, it is time consuming and costly phase. For this one of the cost effective technique can be used which predicts a module as fault-prone and non-fault prone prior to testing. Predicting a safe module during testing and after development increases the cost of projects. As need to allocate better-test resources for module. This process may take most of the time in preparation and may lose some modules untested which cause accidental failure. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. In this research, we present a novel fault prediction technique that reduces the probability of false (pf) and increases the precision for detection of faulty modules. Software Reliability is becoming an essential attribute of any software system. To improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of fault-prone modules.Software defect prediction model helps in early detection of faults and contribute to their efficient removal and producing a reliable software system.

Software industries have enormous demand for fault prediction of the faulty module and fault removal techniques. Many researchers have developed different fault prediction models to predict the fault at an early stage of the software development life cycle (SDLC). But the state-of-the-art model still suffers from the performance and generalize validation of the models. However, some researchers refer to data mining techniques, machine learning, and artificial intelligence play crucial roles in developing fault prediction models. A recent study stated that metric selection techniques also help to enhance the performance of models. Hence, to resolve the issue of improving the fault prediction model’s performance and validation, we have used data mining, instance selection, metric selection, and ensemble methods to beat the state-of-the-art results. For the validation, we have collected the 22 software projects from the four different software repositories. We have implemented three machine learning algorithms and three ensemble methods with two metric selection methods on 22 datasets. The statistical evaluation of the implemented model performed using Wilcoxon signed-rank test and the Friedman test followed by the Nemenyi test to find the significant model. As a result, the Random forest algorithm produces the best result with an average median of 95.43% (accuracy) and 0.96 (f-measure) on 22 software projects. Based on the Nemenyi test, Random forest (RF) is performing better with 4.54 (accuracy mean score) and 4.41 (f-measure mean score) shown in the critical diagram. Experimental study shows that data mining techniques with PCA provide better accuracy and f-measure.

Cybersecurity risks are increasing in frequency and complexity, but many organizations struggle to plan and implement adequate protections at all stages of the software development life cycle (SDLC). Security is frequently added at the end of development (afterthought), and making effective use of safeguard space is difficult for IT leaders. The purpose of this study is to produce an all‐encompassing framework to adopt and ensure security throughout each phase of the SDLC, from planning through maintenance. The aim is to minimize vulnerabilities and improve the resilience of software by making “security by design” a structure that not only adopts security elegantly as a living document but also is built to be part of the development process. This study adopted a mixed‐methods approach. The initial stage of inquiry involved a systematic literature review (SLR) to identify common cybersecurity issues associated with each SDLC phase. The SLR was followed by an empirical survey of 71 software professionals from a variety of organizations. The survey was designed to gather perceived threats, current practices, and challenges associated with software development for survey participants' organizations. The data collected were analyzed and reviewed statistically, through chi‐square tests and ANOVA, to profile the variance relative to the size of the organization, geographic region, and experience level of the practitioner. The results noted several high‐risk challenges across the SDLC: underfunded security controls, imprecise requirements, insecure architecture, software bugs (i.e., injection vulnerabilities), inadequate testing, misconfigured production environments, and unreliable maintenance. The proposed framework provides cybersecurity mitigation techniques for each stage of the SDLC, such as leveraging security‐oriented design patterns, secure coding policies (i.e., input validation and authentication protocols), robust testing (i.e., penetration testing and code review), and continuous monitoring after deployment. The implementation of these measures leads to a significant risk reduction in the overall organizational security posture. The framework is a formalized end‐to‐end approach to secure software development by embedding security throughout the cycle. Embedding security as a part of the process versus an afterthought at every stage of the cycle creates a risk reduction impact. This integrated approach also provides organizations with the opportunity to foresee and mitigate events earlier in the cycle, along with general compliance mandates (i.e., GDPR, HIPAA, and PCI‐DSS), to provide more resilient, trustworthy software systems.

Software Defect Prediction (SDP) plays an active area in many research domain of Software Quality of Assurance (SQA). Many existing research studies are based on software traditional metric sets and defect prediction models are built in machine language to detect the bug for limited source code line. Inspired by the above existing system. In this paper, defect prediction is focused on predicting defects in source code. The objective of this thesis is to improve the software quality for accurate defect prediction is source code for file level. So, that it helps the developer to find the bug and fix the issue, to make better use of a resource which reduces the test effort, minimize the cost and improve the quality of software. A new approach is introduced to improve the prediction performance of Bidirectional RNNLM in Deep Neural Network. To build the defect prediction model a defect learner framework is proposed and first it need to build a Neural Language Model. Using this Language Model it helps to learn to deep semantic features in source code and it train & test the model. Based on language model it combined with software traditional metric sets to measure the code and find the defect. The probability of language model and metric set Cross-Entropy with Abstract Syntax Tree (CE-AST) metric is used to evaluate the defect proneness and set as a metric label. For classification the metric label K-NN classifier is used. BPTT algorithm for learning RNN will provide additional improvement, it improves the predictions performance to find the dynamic error.

Software testing and defect prediction are widely recognized quality assurance activities that can contribute to the effectiveness and efficiency of Just-In-Time Software Quality Assurance (JIT-SQA) by automating processes and reducing software maintenance costs. However, most studies treat them separately, with limited exploration of how defect prediction can guide testing and how testing can support prediction. Existing methods are not tailored for JIT-SQA and often ignore change-specific characteristics such as concept drift and verification latency, leading to unstable defect prediction. Moreover, test annotations on recent code changes are rarely used to update models, missing an opportunity to improve accuracy. Beyond this, effectively leveraging test-annotated samples remains an open research question. On the one hand, these samples frequently suffer from one-sided label noise, as instances that are not identified as defective during testing may still contain defects. On the other hand, defect-inducing changes are relatively infrequent, leading to a significant class imbalance issue. Therefore, exploring how to utilize test annotations to mitigate class imbalance and enhance prediction robustness presents a critical challenge. To fill this research gap, we propose an automatic JIT-SQA framework that incorporates defect prediction and testing processes collaboratively. Specifically, we direct testing activities through defect prediction models and design a novel update mechanism that integrates waiting time with testing feedback to update the predictor in real time. Moreover, we further design a reinforcement learning-based dynamic selection mechanism to mitigate the instability in defect prediction performance over time. Our framework facilitates mutual guidance between automatic testing and defect prediction, leveraging shared knowledge within the JIT-SQA system. This integration holds significant potential for producing high-quality software within modern development timelines. Notably, this study is the first to investigate the integrated application of these two quality assurance activities within a unified framework for JIT-SQA throughout the software development process. We conducted experiments using both statically typed (Java) and dynamically typed (Python) languages by integrating various testing techniques. The results indicate that, after implementing the novel update and dynamic selection mechanisms, our framework significantly outperforms simple integrated frameworks of defect prediction and testing. Specifically, the Recall improves by 21.6%, 10.5%, and 6.3% compared with the testing-integrated versions of ODaSC, ECo-HumLa, and HumLa, respectively, while the F1 score improves by 13.2%, 7.0%, and 5.1%. At the same time, in terms of guiding testing activities, the total number of tests is reduced by 2.9%, 19.5%, and 24.8% compared with the same baselines. These findings demonstrate that our framework not only mitigates the adverse effects of concept drift, verification latency, and class imbalance in code evolution scenarios but also effectively guides testing to reduce resource consumption.

Software Quality Assurance (SQA) is essential in software development and many defect prediction methods based on machine learning have been proposed to identify defective modules. However, most existing defect prediction models do not provide good defect prediction results, and the semantic features reflecting the detective patterns may not be well-captured via traditional feature extraction methods. More information such as code comments should be also be embedded to generate semantic features respecting the source code functionality. Therefore, how to embed code comments for defect prediction is a big challenge, and another problem is that many comments of source code are missing in real-world applications. In this paper, we propose a novel defect prediction model named CAP-CNN (Convolutional Neural Network for Comments Augmented Programs), which is a deep learning model that automatically embeds code comments in generating semantic features from the source code for software defect prediction. To overcome the missing comments problem, a novel training strategy is used in CAP-CNN that the network encodes and absorb comments information to generate semantic features automatically during training process, which does not need testing modules to contain comments. Experimental results on several widely-used software data sets indicate that the comment features are able to improve defect prediction performance.

A fuzzy logic based approach for phase-wise software defects prediction using software metrics

In this paper, a multistage model for software defect density indicator is proposed using the top most reliability relevant metrics and Fuzzy Inference System (FIS). Prediction of defect in each phase of software development life cycle (SDLC) is desirable for effective decision-support and trade-off analysis during early development phases. The predictive accuracy of proposed model is validated using nine real software projects data. Validation results are satisfactory.

The demand for automated online software systems is increasing day by day, which triggered the need for high-quality and maintainable softwares at lower cost. Software defect prediction is one of the crucial tasks of the quality assurance process which improves the quality at lower cost by reducing the overall testing and maintenance efforts. Early detection of defects in the software development life cycle (SDLC) leads to the early corrections and ultimately timely delivery of maintainable software, which satisfies the customer and makes him confident towards the development team. In the last decade, many machine learning-based approaches for software defect prediction have been proposed to achieve the higher accuracy. Artificial Neural Network (ANN) is considered as one of the widely used machine learning techniques, which is included in most of the proposed defect prediction frameworks and models. This research provides a critical analysis of the latest literature, published from year 2015 to 2018 on the use of Artificial Neural Networks for software defect prediction. In this study, a systematic research process is followed to extract the literature from three widely used digital libraries including IEEE, Elsevier, and Springer, and then after following a thorough process, 8 most relevant research publications are selected for critical review. This study will serve the researchers by exploring the current trends in software defect prediction with the focus on ANNs and will also provide a baseline for future innovations, comparisons, and reviews.

An empirical framework for defect prediction using machine learning techniques with Android software

Software development is a collaborative process in which programmers build software by integrating all the stages of the software development life cycle (SDLC). A software repository is a central file storage location where various software packages are stored, and these packages are retrieved and shared between all the software development team members at various locations. The software repositories are divided into various categories based on cooperation, coordination, and communication among the stakeholders as well as evolutionary changes to various software arti-facts such as source code repositories, software bug repositories, historical repositories, run-time repositories and requirement documents, and other documentation. The software bug repository is an essential repository among the entire repositories since the completion of the software is entirely dependent on the bug fixing mechanism associated with this repository in software development. Today's software systems are larger and more complex as they go through various stages from the requirement 98analysis phase to the maintenance phase. A variety of tasks and activities are carried out in each stage of software development, and these are expensive and vulnerable to errors. During software development, a large number of software bugs are continuously generated, and that has become the main reason for the delay in software completion. Hence, there is a vast demand for computational intelligent techniques to accomplish various tasks of software development. In recent years, fuzzy logic techniques emerged and played an important role in various fields of data mining and text mining. Since most of the content related to software bug repositories is text in nature, it is possible to effectively use fuzzy logic techniques to analyze these software bugs.

Early software defect prediction: A systematic map and review

In Software Testing, there are typically two ways to predict defects in the software—within-project defect prediction (WPDP) and cross project defect prediction (CPDP). In this research, we are using a hybrid model for cross project defect prediction. It is a two-phase model consisting of ensemble learning (EL) and genetic algorithm (GA) phase. For our research, we used datasets from the PROMISE repository and created clusters after normalization using k-means clustering algorithm. This further helped us improve the accuracy of the model. Our dataset consists of 22 attributes and were labeled defective or not. Our results show that our hybrid model after implementing k-means clustering achieved an F1 score of 0.666. CPDP is a newer and faster approach for software defect prediction but is often error prone. This method can change the software industry as it will lead to improved software development and faster software delivery.KeywordsCross project defect predictionk-means clusteringEnsemble learningGenetic algorithmCompositional modelEnsemble model