Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques - Fault Prediction Techniques

Aug 5, 2026

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Software increasingly supports everyday life, making software development more complex and quality-critical. High testing cost motivates early defect discovery, since late detection significantly increases time and expenses. This paper addresses software fault prediction by reviewing machine learning techniques used to classify software bugs and forecast faults before formal testing. It explains how defect prediction supports prioritizing testing, assessing quality against standards, and guiding resource allocation for verification.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Software defects and cost of testing

Role of defect prediction in quality assurance

Fault-prone module identification and ML shift

Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques Nikita Gupta1, Ripu Ranjan Sinha2, Ankur Goyal3, Neelam Sunda4 and Divya Sharma5 1Department of Computer Science, RTU, Kota, Rajasthan, India, [nikitagupta.ssm@gmail.com](nikitagupta.ssm@gmail.com) 2Department of Computer Science, RTU, Kota, Rajasthan, India, [drsinhacs@gmail.com](drsinhacs@gmail.com) 3Department of Computer Science, Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, Maharashtra, India, [ankur_gg5781@yahoo.co.in](ankur_gg5781@yahoo.co.in) 4Department of Computer Science, RTU, Kota, Rajasthan, India, [research.neelam@gmail.com](research.neelam@gmail.com) 5Assistant Professor, Kanoria PG Mahila Mahavidyalaya, Jaipur, Rajasthan, India Abstract—Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper. Keywords-Fault, Machine Learning, Decision Tree, SVM, KNN, Ensemble Techniques,Software,Fault detection model. I. INTRODUCTION An error state that does not adhere to the software specifications or user expectations is referred to as a software defect in a software system. Unexpected and frequently inaccurate results produced by the programme are the result of a logic or coding error. During the programming the programmer or the software designer may make mistake, most of the errors are due to the such type of mistakes. The following are examples of flaws in software systems in reality: Arithmetic errors that arise from mistakes in certain arithmetic expressions; syntax errors brought on by the way the code was written. Logical errors are errors in the code's implementation, Performance flaws result in undesirable outcomes, Interaction between users and the software results in interface defects [1] . The availability and dependability of software systems are gravely threatened by software defects. Finding and fixing the system's flaws is very expensive once the flawed system has been put into place. By gaining crucial knowledge about the kind and location of defects, developers and programmers can profit from the prediction of unknown defects and increase the necessary level of confidence in the system. Prediction of software system flaws is currently one of the most researched topics by researchers [2] . Defect predictions, which assist programmers in locating bugs in malfunctioning code regions, allow programmers to prioritise their testing techniques according to the severity of the problematic code regions. Defect Prediction enables software testers and developers to evaluate the product's quality, determine whether or not quality standards are met, and determine whether the finished product satisfies users' needs and expectations. Additionally, it makes it easier to distribute resources for the system's formal verification as it is being developed [3] . Software has evolved into a crucial part of many systems as a result of the use of computer technology. The creation of these systems is becoming more difficult as softwa

FAQs

Why is early fault prediction important in software development?

Nearly one-third of software development cost is spent on testing, so finding bugs early reduces time and expense. Early prediction helps developers avoid higher remediation costs later.

What is the main goal of software fault prediction systems?

To identify fault-prone modules as accurately as possible before system testing. This enables better allocation of testing resources.

Which machine learning approaches are discussed for classifying software bugs?

The paper reviews multiple machine learning techniques for bug classification, including decision tree methods, SVM, KNN, and ensemble techniques.

Aug 5, 2026

Upload by Patrick