Software Fault Prediction Using Two -Stage Data Preprocessing Method

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.

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.

Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning (ML) has emerged as a transformative solution, introducing predictive and adaptive capabilities that optimize test case selection, automate defect detection, and enhance overall software quality assurance (QA). This study explores the integration of ML in software testing, addressing the challenges of traditional QA methodologies and demonstrating how AI-driven frameworks improve testing efficiency. Methodology - To investigate the impact of ML in software testing, this research adopts a systematic approach by analyzing ML-driven test automation techniques, including predictive testing, adaptive test execution, and automated test case generation. Research reviews how Google Microsoft Facebook IBM and Deep Code put ML-based quality assurance frameworks into operation. The study leverages supervised learning, reinforcement learning, deep learning, and NLP-based techniques to demonstrate how ML models predict software defects, dynamically adapt test cases, and optimize testing resources. The research tests how ML-based testing models operate within CI/CD pipelines to improve ongoing testing and deployment flow. Analysis & Results - The analysis of ML-driven software testing reveals that predictive analytics improves early defect detection rates. It helps developers spend 37% less time debugging their work. Adaptive testing models, including self-healing test scripts, minimize maintenance costs by 50% and enhance test reliability in agile environments. The integration of NLP-based test case generation increases test coverage. NLP technology enables automatic connection between requirements and test cases at 89% success rate. Additionally, reinforcement learning techniques improve test case selection, reducing redundant test executions by 43%. Our research shows different ML methods work well to lessen incorrect error alerts. ML integration for QA surely increasing defect prediction accuracy and optimizing test execution time. Findings & Contributions - This research contributes to the field of AI-driven software testing by providing a comprehensive framework for ML-based QA methodologies. Our study shows that machine learning helps find more software problems better adapts test cases and lowers testing expenses to solve present software development needs. The study also identifies critical challenges, including data availability, model interpretability, and computational overhead, suggesting future research directions in Explainable AI (XAI), hybrid AI-ML testing models, and AI-driven security testing. As the industry moves toward AI-first software testing, this research paves the way for fully autonomous QA frameworks, enabling intelligent, scalable, and cost- effective software validation techniques. Keywords - Machine Learning, Software Testing, Quality Assurance, Predictive Testing, Adaptive Testing, Test Automation, Defect Prediction, Self- Healing Test Scripts, AI-Driven QA, Reinforcement

Software reliability engineering involves techniques for the design, testing and evaluation of software systems, focusing on reliability attributes. Design for reliability is achieved by fault-tolerance techniques that keep the system working in the presence of software faults. Testing for reliability is achieved by fault-removal techniques that detect and correct software faults before the system is deployed. Evaluation for reliability is achieved by fault-prediction techniques that model and measure the reliability of the system during its operation. This paper presents the best current practices in software reliability engineering for design, evaluation purposes. There are descriptions how fault-tolerant components are designed and applied to software systems, how software testing schemes are performed to show improvement of software reliability, and how reliability quantities are obtained for software systems. The tools associated with these techniques are also examined, and some application results are described.

Software testing is a critical and time-consuming element of the software development life cycle (SDLC). Software testing determines the quality of a software before it is used by end users. The main goal is to find software errors or defects so that these errors or defects can be corrected in this testing phase. There are two general techniques for detecting software errors, namely: Black Box and White Box Testing. Black Box testing technique is used for validation, namely whether you have built the right software and White Box testing is used for verification, namely whether the software made is correct. This study aims to test the value index software using three methods of Black Box testing techniques, namely Equivalence partitioning (EP), Boundary Value Analysis (BVA) and Error Guessing. The testing process is carried out to find out how much error occurs in the value index software. The research method is qualitative descriptive and analytical. The results of this study indicate that the value index application has no shortcomings (error of 0%) both with BVA, EP and Error Guessing techniques.

A novel approach for software reliability analysis of digital instrumentation and control systems in nuclear power plants

Quantitative software reliability assessment methodology based on Bayesian belief networks and statistical testing for safety-critical software

Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication is to provide a framework which is expected to be more effective and acceptable for predicting the defects in multiple phases across software development lifecycle. The proposed framework is based on the use of neural networks for predicting defects in software development life cycle. Further, in order to facilitate the easy use of the framework by project managers, a software graphical user interface has been developed that allows input data (including effort and defect) to be fed easily for predicting defects. The proposed framework provides a probabilistic defect prediction approach where instead of a definite number, a defect range (minimum, maximum, and mean) is predicted. The claim of efficacy and superiority of proposed framework is established through results of a comparative study, involving the proposed frame-work and some well-known models for software defect prediction.

Software quality assurance and testing is a vital phase in the software development lifecycle, oftentimes requiring a significant portion of the overall development budget. Providing authentic, real-world software quality assurance and testing experiences within the context of a Computer Science or Software Engineering curriculum is challenging. Based on prior experience leveraging Free and Open Source Software (FOSS) as an instructional medium for teaching Software Engineering concepts, this paper describes five increasingly involved learning interventions that utilize FOSS in a software testing course to provide students real-world hands-on software testing knowledge and experience. The proposed approach will be piloted and assessed in software testing courses at the undergraduate and graduate level.

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 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.

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

Software testing provides a means to reduce errors, cut maintenance and overall software costs. Numerous software development and testing methodologies, tools, and techniques have emerged over the last few decades promising to enhance software quality. While it can be argued that there has been some improvement it is apparent that many of the techniques and tools are isolated to a specific lifecycle phase or functional area. This paper presents a set of best practice models and techniques integrated in optimized and quantitatively managed software testing process (OptimalSQM), expanding testing throughout the SDLC. Further, we explained how can Quantitative Defect Management Model be enhanced to be practically useful for determining which activities need to be addressed to improve the degree of early and cost-effective software fault detection with assured confidence is proposed. To enable software designers to achieve a higher quality for their design, a better insight into quality predictions for their design choices, test plans improvement using Simulated Defect Removal Cost Savings model is offered in this paper.

As the time passes the software complexity is increasing and due to this software reliability and quality will be affected. And for measuring the software reliability and quality various defect measurement and defect tracing mechanism will be used .Software defect prevention work typically focuses on individual inspection and testing technique. ODC is a mechanism by which we exploit software defect that occur during the software development life cycle. Orthogonal defect classification is a concept which enables developers, quality managers and project managers to evaluate the effectiveness and correctness of the software

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.