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Journal articles on the topic 'Software defect'

Author: Grafiati

Published: 30 May 2022

Last updated: 27 July 2025

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1

Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to

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2

Kumaresh, Sakthi, and R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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â€œQuality is never an accident; it is always the result of intelligent effortâ€ [10]. In the process of making quality software product, it is necessary to have effective defect prevention process, which will minimize the risk of making defects /errors in software deliverables. An ideal approach would involve effective software development process with an integrated defect prevention process. This paper presents a Defect Prevention Model in which Defect Prevention Process(DPP) is integrated into software development life cycle to reduce the defects at early stages itself, thereby reducing th

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Malhotra, Ruchika, and Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques." International Journal of Software Engineering and Knowledge Engineering 31, no. 02 (2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

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Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build de

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Zhang, Wei, Zhen Yu Ma, Wen Ge Zhang, Qing Ling Lu, and Xiao Bing Nie. "Correlation Analysis of Software Defects Density and Metrics." Applied Mechanics and Materials 713-715 (January 2015): 2225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2225.

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It is very useful for improving software quality if we can find which software metrics are more correlative with software defects or defects density. Based on 33 actual software projects, we analyzed 44 software metrics from application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defects, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation wit

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Sakthi, Kumaresh, and Ramachandran Baskaran. "Defect Prevention Based on 5 Dimensions of Defect Origin." International Journal of Software Engineering & Applications (IJSEA) 3, no. 4 (2020): 87–98. https://doi.org/10.5281/zenodo.4047989.

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“Discovering the unexpected is more important than confirming the known [7]. In software development, the “unexpected” one relates to defects. These defects when unattended would cause failure to the product and risk to the users. The increasing dependency of society on software and the crucial consequences that a failure can cause requires the need to find out the defects at the origin itself. Based on the lessons learnt from the earlier set of projects, a defect framework highlighting the 5 Dimensions (Ds) of defect origin is proposed in this work. The defect framework is b

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Diwan, Sinan, and Abdul Syukor Mohamad. "Machine Learning Empowered Software Prediction System." Wasit Journal of Computer and Mathematics Science 1, no. 3 (2022): 54–64. http://dx.doi.org/10.31185/wjcm.61.

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Prediction of software defects is one of the most active study fields in software engineering today. Using a defect prediction model, a list of code prone to defects may be compiled. Using a defect prediction model, software may be made more reliable by identifying and discovering faults before or during the software enhancement process. Defect prediction will play an increasingly important role in the design process as the scope of software projects grows. Bugs or the number of bugs used to measure the performance of a defect prediction procedure are referred to as "bugs" in this context. Def

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Han, Wan Jiang, He Yang Jiang, Yi Sun, and Tian Bo Lu. "Software Defect Distribution Prediction for BOSS System." Applied Mechanics and Materials 701-702 (December 2014): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.67.

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Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects for BOSS project, which applies defect distribution model. Experiment results show that this approach can effectively improve the accuracy of defect prediction.

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8

Simatupang, Johannes. "Software Defect Elimination Information System in Software House Company." Jurnal Indonesia Sosial Teknologi 5, no. 8 (2024): 2936–45. http://dx.doi.org/10.59141/jist.v5i8.1289.

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In software that is free from defects or errors, a fixed method and technique of software testing is required. For this step, an Information System is needed to help eliminate software defects, so that testing or testing work is not a burden on software costs but is a perfection of software development work so that Zero Defect Application Software can be realized. From several software testing methods and techniques, a strategy suitable for the size of a software development project is required. So even though the software development project is small, it still requires a suitable test. And fo

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9

WANG, Qing. "Software Defect Prediction." Journal of Software 19, no. 7 (2008): 1565–80. http://dx.doi.org/10.3724/sp.j.1001.2008.01565.

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10

Malhotra, Ruchika, and Madhukar Cherukuri. "Convolutional Neural Networks for Software Defect Categorization: An Empirical Validation." JUCS - Journal of Universal Computer Science 31, no. (1) (2025): 22–51. https://doi.org/10.3897/jucs.117185.

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The escalating complexity and scale of software systems have rendered them increasingly susceptible to a variety of defects. To empower maintenance teams to efficiently prioritize and resolve defects, Software Defect Categorization (SDC) models have emerged, offering the classification of software defects into categories such as "high," "medium," or "low." This study embarks on the development of SDC models, based on three critical defect attributes: i) the maintenance effort required to rectify a defect, ii) the change impact on the software induced by defect resolution, and iii) a combined a

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11

Kumar, Swadesh, Rajesh Kumar Singh, and Awadhesh Kumar Maurya. "Software Defect Prediction: State of the Art Survey." International Journal of Innovative Technology and Exploring Engineering 11, no. 7 (2022): 32–35. http://dx.doi.org/10.35940/ijitee.g9993.0611722.

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Software has evolved into a critical component in today's world. The quantity of faults in a software product is connected to its quality, which is also restricted by time and cost. In terms of both quality and cost, software faults are costly. The practice of tracing problematic components in software prior to the product's launch is known as software defect prediction. Defects are unavoidable, but we should strive to keep the number of defects to a bare minimum. Defect prediction results in shorter development times, lower costs, less rework, higher customer satisfaction, and more dependable

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12

Yao, Wenjun, Muhammad Shafiq, Xiaoxin Lin, and Xiang Yu. "A Software Defect Prediction Method Based on Program Semantic Feature Mining." Electronics 12, no. 7 (2023): 1546. http://dx.doi.org/10.3390/electronics12071546.

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As the size and complexity of software systems grow, knowing how to effectively judge whether there are defects in the programs has attracted extensive attention in research. However, current software defect prediction methods only extract semantic information at the syntactic level and lack features to mine defect manifestations at the semantic level of code, because defective software is incomplete or defective in semantic representation. Defective software exhibits incomplete or flawed semantic behavior. This paper proposes a software defect prediction method based on the program semantics

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13

Misirli, Ayse Tosun, Ayse Bener, and Resat Kale. "AI-Based Software Defect Predictors: Applications and Benefits in a Case Study." AI Magazine 32, no. 2 (2011): 57. http://dx.doi.org/10.1609/aimag.v32i2.2348.

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Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects usin

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14

Tosun, Ayse, Ayse Bener, and Resat Kale. "AI-Based Software Defect Predictors: Applications and Benefits in a Case Study." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 2 (2010): 1748–55. http://dx.doi.org/10.1609/aaai.v24i2.18807.

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Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The application of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defect

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15

Swadesh, Kumar, Kumar Singh Rajesh, and Kumar Maurya Awadhesh. "Software Defect Prediction: State of the Art Survey." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 7 (2022): 32–35. https://doi.org/10.35940/ijitee.G9993.0611722.

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<strong>Abstract</strong>: Software has evolved into a critical component in today's world. The quantity of faults in a software product is connected to its quality, which is also restricted by time and cost. In terms of both quality and cost, software faults are costly. The practice of tracing problematic components in software prior to the product's launch is known as software defect prediction. Defects are unavoidable, but we should strive to keep the number of defects to a bare minimum. Defect prediction results in shorter development times, lower costs, less rework, higher custome

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16

Malhotra, Ruchika, and Madhukar Cherukuri. "Convolutional Neural Networks for Software Defect Categorization: An Empirical Validation." JUCS - Journal of Universal Computer Science 31, no. 1 (2025): 22–51. https://doi.org/10.3897/jucs.117185.

Full text

Abstract:

The escalating complexity and scale of software systems have rendered them increasingly susceptible to a variety of defects. To empower maintenance teams to efficiently prioritize and resolve defects, Software Defect Categorization (SDC) models have emerged, offering the classification of software defects into categories such as &quot;high,&quot; &quot;medium,&quot; or &quot;low.&quot; This study embarks on the development of SDC models, based on three critical defect attributes: i) the maintenance effort required to rectify a defect, ii) the change impact on the softwa

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17

Wang, Yan. "Efficient Prediction Method of Defect of Monitor Configuration Software." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (2019): 340–44. http://dx.doi.org/10.20965/jaciii.2019.p0340.

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In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of softwar

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18

CHANG, CHING-PAO. "INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (2013): 147–72. http://dx.doi.org/10.1142/s0218194013500022.

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Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendation

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19

Falessi, Davide, Aalok Ahluwalia, and Massimiliano DI Penta. "The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects." ACM Transactions on Software Engineering and Methodology 31, no. 1 (2022): 1–26. http://dx.doi.org/10.1145/3467895.

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Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can ca

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20

SCHNEIDEWIND, NORMAN. "COMPLEXITY-DRIVEN RELIABILITY MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 05 (2008): 479–94. http://dx.doi.org/10.1142/s0218539308003179.

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A model of software complexity and reliability is developed that uses an evolutionary process to transition from one software system to the next while complexity metrics are used to predict the reliability for each system. Systems are tested until the software passes defect presence criteria and is released. Testing criteria are based on defect count, defect density, and testing efficiency predictions exceeding specified thresholds. In addition, another type of testing efficiency — a directed graph representing the complexity of the software and defects embedded in the code — is used to evalua

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21

Liu, Can, Sumaya Sanober, Abu Sarwar Zamani, L. Rama Parvathy, Rahul Neware, and Abdul Wahab Rahmani. "Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network." Security and Communication Networks 2022 (April 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/5058461.

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Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction t

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Liu, Can, Sumaya Sanober, Abu Sarwar Zamani, L. Rama Parvathy, Rahul Neware, and Abdul Wahab Rahmani. "Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network." Security and Communication Networks 2022 (April 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/5058461.

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Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction t

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23

Huh, Sang Moo, and Woo-Je Kim. "The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects." Applied Sciences 10, no. 19 (2020): 6946. http://dx.doi.org/10.3390/app10196946.

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As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects. However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects. Therefore, the present study collected embedded softw

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Fanqi Meng, Fanqi Meng, Wenying Cheng Fanqi Meng, and Jingdong Wang Wenying Cheng. "An Integrated Semi-supervised Software Defect Prediction Model." 網際網路技術學刊 24, no. 6 (2023): 1307–17. http://dx.doi.org/10.53106/160792642023112406013.

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<p>A novel semi-supervised software defect prediction model FFeSSTri (Filtered Feature Selecting, Sample and Tri-training) is proposed to address the problem that class imbalance and too many irrelevant or redundant features in labelled samples lower the accuracy of semi-supervised software defect prediction. Its innovation lies in that the construction of FFeSSTri integrates an oversampling technique, a new feature selection method, and a Tri-training algorithm, thus it can effectively improve the accuracy. Firstly, the oversampling technique is applied to expand the class of inadequate

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Choudhury, Madhab Paul. "A Framework for Developing Correct Software Programs through Software Defect Prediction and Elimination using Machine Learning Models." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 402–10. http://dx.doi.org/10.22214/ijraset.2024.64162.

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Software Defect Prediction [SDP] is an important item for development of mistake free software. A software defect is an error, bug, flaw, fault, malfunction or mistakes. If software defect is present in the software, the output produced from that software becomes erroneous. If defect is present in the software, time, cost, effort will be lost and there will be wastage of resources. Therefore, it is necessary is to determine the defects in an early phase of software development. For this purpose, Kaggle software defect data set(ant-1.3) [11] have been used. Machine learning models like Random F

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Pamuji, Agus. "Analisa Studi Empirik Kerangka Kerja Pengukuran Kualitas Perangkat Lunak Bebas Cacat." Jurnal Informatika: Jurnal Pengembangan IT 3, no. 1 (2018): 130–35. http://dx.doi.org/10.30591/jpit.v3i1.664.

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Testing activitiy is a strategic step to determine software quality was generated, so that is accepted by the end user. In the testing an errors were found that may be cause to risk a defect on the software. This study was conducted by establishing a measurement framework to analyze software metrics test toward risk prediction of defects consisting of defect density, defect removal, and Line of code. In the analysis, the data set contains 53 module samples through a statistical approach with correlation analysis techniques. Based on the hypothesis were proposed, that there are only 2 of 3 item

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Sharma, Dr Rakesh T., and Dr Neha R. Kulkarni. "GUIDING SEARCH-BASED SOFTWARE TESTING WITH DEFECT PREDICTION: AN EMPIRICAL INVESTIGATION." International Journal of Modern Computer Science and IT Innovations 2, no. 3 (2025): 9–17. https://doi.org/10.55640/ijmcsit-v02i03-02.

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Search-Based Software Testing (SBST) has emerged as a powerful technique for automated test case generation, effectively achieving high code coverage. However, maximizing code coverage does not always correlate directly with the ability to detect real faults. This paper presents an empirical investigation into the effectiveness of using theoretical defect predictors to guide the search process in SBST, aiming to enhance its fault-finding capability. We propose integrating defect prediction models, which identify fault-prone software modules based on static code and change metrics, into the fit

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Dos Santos, Rodrigo Alexandre. "Just-In-Time Software Defect Prediction using a deep learning-based model." New Trends in Computer Sciences 2, no. 2 (2024): 91–100. https://doi.org/10.3846/ntcs.2024.22274.

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The increase in software complexity, driven by technological developments and user demands, has created major challenges for companies in Software Quality Assurance. Companies seek efficient ways to identify and mitigate defects, recognizing that they cause high financial costs and other problems with negative impacts on business. Among defect prediction approaches, Just-In-Time Software Defect Prediction has received increased attention from software industry professionals in recent years. This technique aims to identify and treat defects early, to improve the quality of the software developm

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Zhang, Wei, Zhen Yu Ma, Qing Ling Lu, Xiao Bing Nie, and Juan Liu. "Research on Software Defect Prediction Method Based on Machine Learning." Applied Mechanics and Materials 687-691 (November 2014): 2182–85. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2182.

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This paper analyzed 44 metrics of application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defect, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density. On the basis of feature selection, we predicted defect density with 16 machine learning models for 33 actual software projects. The results show that the Spearman rank correl

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LIU, Hai, and Ke-gang HAO. "Defining software defect data." Journal of Computer Applications 28, no. 1 (2008): 226–28. http://dx.doi.org/10.3724/sp.j.1087.2008.00226.

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31

Hall, Robert J. "Editorial: software defect detection." Automated Software Engineering 17, no. 3 (2010): 213–15. http://dx.doi.org/10.1007/s10515-010-0071-y.

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Jones, C. "Software defect-removal efficiency." Computer 29, no. 4 (1996): 94–95. http://dx.doi.org/10.1109/2.488361.

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Ghorai, Aindrila. "Static Testing in Software Engineering - Reducing Defect Leakage." International Journal of Science and Research (IJSR) 7, no. 5 (2018): 1862–65. http://dx.doi.org/10.21275/sr24509123132.

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Bayramova, Tamilla. "SOFTWARE DEFECT PREDICTION USING THE MACHINE LEARNING METHODS." Problems of Information Technology 14, no. 2 (2023): 23–31. http://dx.doi.org/10.25045/jpit.v14.i2.03.

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Reliability of software systems is one of the main indicators of quality. Defects occurring when developing software systems have a direct effect on reliability. Precise prediction of defects in software systems helps software engineers to ensure the reliability of software systems and to properly allocate resources for the trial process. The development of an ensemble method by combining several classification methods occupies one of the main places in research conducted in the field of error prediction in software modules. This paper proposes a method based on the application of ensemble tra

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Marappan, Shanmugasundaram, Archana Kollu, Ismail Keshta, Shehab Mohamed Beram, Sahil Bhende, and Karthikeyan Kaliyaperumal. "An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software." Scientific Programming 2022 (September 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/2302027.

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The resolution of a software bug depends on the severity of the defect report. Open-source software defect tracking solutions have taken over as the principal means of processing enormous amounts of defect information data due to the ongoing increase in software scale. Dealing with software faults requires analyzing the implications of defect report severity in the data warehouse. Thus, the authors have proposed an optimized systematic approach through the research and analysis of Bugzilla defect tracking system data in this study, where it is found that the attribute characteristics of differ

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Wang, Hong, and Limin Yuan. "Software engineering defect detection and classification system based on artificial intelligence." Nonlinear Engineering 11, no. 1 (2022): 380–86. http://dx.doi.org/10.1515/nleng-2022-0042.

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Abstract With the increasing reliance on automatic software-based applications, it is important to automate the classification of software defects and ensure software reliability. An automatic software defect classification system based on an expert system is proposed in this article. In this method, DACS first determines the category of software defects through the selection of typical features, then reduces the spatial knowledge base searched by the inference engine and selects the characteristics of a certain type of defect. Make a selection, determine the name of the defect, and finally se

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Chouhan, Charulata. "Design and Deployment of a Machine Learning Model for Software Defect Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42277.

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This paper explores software defects as an inherent aspect of software products, significantly impacting software quality. Defects, often deviations from specifications, can lead to functionality failures. Ensuring software quality assurance is complex and time-consuming, with many projects lacking sufficient resources to eliminate all defects before release. This can affect product quality and an organization's reputation. To address this challenge, various techniques for software defect prediction are employed. This research utilizes pre-processing, feature extraction, and classification met

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V., Ruckmani, and Prakasam S. "Comparative Study of Software Defect Prediction and Analysis the Class using Machine Learning Method." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1313–18. https://doi.org/10.35940/ijeat.E1161.069520.

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An automatic mode that increases sample stability is checked to verify the software design. Predict software flaws are the main focus of the engineering department. Computational software engineering is one of the active study areas of a software flaw. Depending on the metric, software quality and the efficient allocation of volume resources can easily improve defect quality, thus reducing costs. Many data mining and datasets can be used to store defect prediction software. Machine learning software defect prediction technology is an important branch of the computer. Therefore, in this method

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Mehta, Sweta, Pankaj K. Goswami, and K. Sridhar Patnaik. "Network Embedding Techniques for Predicting Software Defects: A Review." International Journal of Scientific Research and Management (IJSRM) 13, no. 06 (2025): 2254–75. https://doi.org/10.18535/ijsrm/v13i06.ec05.

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In the software development process, ensuring the quality of the software is essential. Software defect prediction (SDP) is of significant importance in identifying software modules with a high likelihood of defects. Several machine learning-based defect prediction models have been developed and implemented in recent years. Researchers have also utilized network embedding for SDP, showcasing the adaptability of Natural Language Processing techniques within the domain of defect prediction. This study aims to review, investigate, and discuss network embedding's use in SDP. We examined the previo

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Khan, Muhammad Adnan, Nouh Sabri Elmitwally, Sagheer Abbas, et al. "Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review." Scientific Programming 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2117339.

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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

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Prasad, V. S., and K. Sasikala. "A Study On Software Engineering Defect Prediction." Data Analytics and Artificial Intelligence 2, no. 1 (2022): 1–6. http://dx.doi.org/10.46632/daai/2/1/1.

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The success of any software system entirely depends on the accuracy of the results of the system and whether it is without any flaws. Software defect prediction problems have an extremely beneficial research potential. Software defects are the major issue in any software industry. Software defects not only reduce the software quality, increase costing but it also suspends the development schedule. Software bugs lead to inaccurate and discrepant results. As an outcome of this, the software projects run late, are cancelled or become unreliable after deployment. Quality and reliability are the ma

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Jindal, Rajni, Ruchika Malhotra, and Abha Jain. "Predicting Software Maintenance Effort by Mining Software Project Reports Using Inter-Version Validation." International Journal of Reliability, Quality and Safety Engineering 23, no. 06 (2016): 1640009. http://dx.doi.org/10.1142/s021853931640009x.

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Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description.

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Ma, Yanfang, Xiaotong Gao, Wei Zhou, and Liang Chen. "The Trustworthiness Measurement Model of Component Based on Defects." Mathematical Problems in Engineering 2022 (December 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/7290001.

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In modern software engineering, the component-based development approach has become one of the important trends in software development technology. The trustworthiness of components plays a vital role in developing component-based trustworthy software. If there exist defects in components, then the trustworthiness of the component will be reduced, and the trustworthiness of the software system will be influenced. In this case, it is necessary to measure the trustworthiness of the component in terms of the defect. In this paper, a trustworthiness measurement model of components will be proposed

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Bejjanki, Kiran Kumar, Jayadev Gyani, and Narsimha Gugulothu. "Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance." Symmetry 12, no. 3 (2020): 407. http://dx.doi.org/10.3390/sym12030407.

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Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SM

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Emmanuel U. Oyo-Ita, Emmanuel A. Edim, Anthony Otiko, and Darlington E. Izuki. "Improving model performance for software defect detection and prediction using ensemble method and cross validation techniques." International Journal of Science and Research Archive 12, no. 2 (2024): 2363–73. http://dx.doi.org/10.30574/ijsra.2024.12.2.1518.

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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 o

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Ok, Chi-Wang. "Relationship Between Obligation to Complete Work and Defect Liability in Software Development Contracts." Institute for Legal Studies Chonnam National University 43, no. 3 (2023): 1–29. http://dx.doi.org/10.38133/cnulawreview.2023.43.3.1.

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Today, as the use of computers, communication, and automation equipment increases, the demand for development of software used in hardware is also increasing, and software development contracts are also frequently signed. In the case of software development contracts, the development requirements or the details of what developers have to do at the time of signing the contract are often not specifically determined in the contract, resulting in a lot of disputes regarding “Whether the work has been completed” or defect liability. Therefore, in this study I examined the content of an obligation t

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Liang, Qi. "Research on Software Defect Prediction Model based on Deep Learning." Highlights in Science, Engineering and Technology 122 (December 15, 2024): 23–29. https://doi.org/10.54097/y0w76b47.

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As software systems grow in complexity and scale, detecting and predicting defects has become crucial for ensuring software quality and enhancing development efficiency. Traditional approaches to software defect prediction rely heavily on manual feature extraction and statistical models, which often struggle to handle intricate defect patterns and large-scale datasets. Recently, deep learning has demonstrated significant promise in software defect prediction, primarily due to its ability to automatically extract features and its strong pattern recognition capabilities. To enhance both the accu

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Haldar, Susmita, and Luiz Fernando Capretz. "Interpretable Software Defect Prediction from Project Effort and Static Code Metrics." Computers 13, no. 2 (2024): 52. http://dx.doi.org/10.3390/computers13020052.

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Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research also assesses the reliability of cross-project defect predic

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Aldabbagh, Ghada Mohammad Tahir, and Safwan Omar Hasoon. "DEFECT SEVERITY CODE PREDICTION BASED ON ENSEMBLE LEARNING." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 4 (2024): 146–53. https://doi.org/10.35784/iapgos.6393.

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In machine learning, learning algorithms that learn from other algorithms are called meta-learning. New algorithms called Ensemble algorithms have surfaced as a viable method to improve defect prediction models' accuracy and dependability. In software development defect prediction of software engineering is still a big challenge, and leads to the failure of systems, increases the cost of maintenance, and makes the development process more difficult. Consequently, defect prediction systems have become more popular as a way to foresee possible flaws early on in the development process. Defect pr

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Rai, Deepti, and Jyothi Arcot Prashant. "A novel approach to enhancing software quality assurance through early detection and prevention of software faults." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 894. https://doi.org/10.11591/ijai.v14.i2.pp894-906.

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The current manuscript presents a predictive mechanism towards analyzing software defects by developing a line-level fault prediction technique. Current methodologies rely on customized attributes and overlook the sophisticated structural and semantic characteristics inherent in programming languages. This oversight often led to suboptimal defect identification, as code defects are intricately scrambled with their contextual environment. Moreover, conventional software defect prediction (SDP) strategies, typically focusing on larger code units such as modules or classes, impede precise error l

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