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\nDeepti Rai, Jyothi Arcot Prashant  \nDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, Ramaiah University of Applied Sciences,  \nBengaluru, India  \nArticle history:  \nReceived Feb 27, 2023 Revised Jul 11, 2023 Accepted Jul 18, 2023  \nKeywords:  \nCost evaluation Machine learning Prediction Software engineering  \nSoftware fault  \nCorresponding Author:  \nSoftware engineering is an integral part ofany software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adoptsa machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN) . These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction.  \nThis is an open access article under the CC BY-SA license.  \nDeepti Rai  \nDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, Ramaiah University of Applied Sciences  \nBengaluru, India  \nEmail: [deeraisecond@gmail.com](deeraisecond@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe advancements in computing methods, embedded system, communication standards, and artificial intelligence (AI) has disrupted the whole ecosystem of the conventional approaches of the software scope [1],[2] . However, the software development process always aims to meet the quality and safety of all software vulnerabilities [3], [4] . The faults that may cause a functional error or vulnerabilities must be detected and removed. However, it can happen only when the faults are localized. Localizing faults in any system, including software, is the most expensive and challenging requirement before handling the faults [5] . Recently, many novel and fundamental approaches have been proposed for fault localization, providing new dimensions and scope [6]–[13] . The broader classification of the fault localization techniques (FLT) is given into two classes: i) static-FLT and ii) dynamic FLT [6] . However, static FLT in the context of software programs checks for bugs or defects in the source code concerning the standard templates of the program models [14] . On the other hand, dynamic-FLTs work on the principle that it does not have prior knowledge of the program and tag only  \nthe correctness based on its run-time behavior [15] . The data-driven approach of the fault locations is significant for achieving fault localization and hence fault prediction.  \nExisting studies carried out in this direction are briefed as follows: The work of McCabe [15] considers a program: \"P\" having a complexity metric: {v} that tests a number of the path: {ac: actual complexity} . The correlation between the 'v' and 'ac' defines whether the program is more error-prone or less. Though this technique is quite fundamental in terms ofa mathematical model, it lacks scalability [1","cbCaikRzuRpMM4UZ","https://ap.wps.com/l/cbCaikRzuRpMM4UZ","pdf",416763,"English","# 1. INTRODUCTION\n## Fault localization background and techniques\n## Machine-learning approaches for fault prediction\n# 2. Method and framework (implied by study)","[{\"question\":\"What problem does the paper address in software engineering?\",\"answer\":\"It targets the need for predictive evaluation of software faults to reduce the cost and difficulty of locating faults in software systems.\"},{\"question\":\"How does the proposed framework estimate cost and predict faults?\",\"answer\":\"It uses a computational scheme based on lines of code and integrates a correlation-based cost model with multiple machine-learning models.\"},{\"question\":\"Which machine-learning model performed best for software fault prediction?\",\"answer\":\"The results indicate that neural networks achieved a significantly lower error score compared with logistic regression and support vector regression.\"}]","A simplified predictive framework for cost evaluation to fault assessment using machine learning | PDF",25]