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035 a| (Sirsi) o1456021880;

035 a| (OCoLC)1456021880;

049 a| EREE;

090 a| Q336;

100 1 a| Cahill, Elaine,e| author.?| UNAUTHORIZED;

245 13 a| An empirical exploration of artificial intelligence for software defect prediction in software engineering /c| by Elaine Cahill.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2024.;

300 a| 1 online resources (89 pages) :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 1.64 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2024.;

502 a| Presented to the Faculty of the Department of Computer Science;

500 a| Advisor: Madhusudan Srinivasan;

500 a| Title from PDF t.p. (viewed December 1, 2025).;

520 3 a| Artificial Intelligence (AI) is an important topic in software engineering not only for data analysis and pattern recognition, but for the opportunity of finding solutions to problems that may not have explicit rules or instructions. Reliable prediction methods are needed because we cannot prove that there are no defects in software. Deep learning and machine learning have been applied to software defect prediction in the attempt to generate valid software engineering practices since at least 1971. Avoiding safety-critical or expensive system failures can save lives and reduce the economic burden of maintaining systems by preventing failures in systems such as aviation software, medical devices, and autonomous vehicles. This thesis contributes to the field of software defect prediction by empirically evaluating the performance of various machine learning models, including Logistic Regression, Random Forest, Support Vector Machine, and a stacking classifier combining these models. The findings highlight the importance of model selection and feature engineering in achieving accurate predictions. We followed this with a stacking classifier that combines Logistic Regression, Random Forest, and Support Vector Machine (SVM) to see if that improved predictive performance. We compared our results with previous work and analyzed which features or attributes appeared to be effective in predicting defects. We end by discussing potential next steps for further research based on our work results.;

504 a| Includes bibliographical references.;

650 0 a| Artificial intelligencex| Data processing.=| ^A120708;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Software failuresx| Preventionx| Data processing.=| ^A455758;

650 0 a| Support vector machines.=| ^A1156583;

650 0 a| Software engineering.=| ^A229299;

650 0 a| Ensemble learning (Machine learning)=| ^A1486579;

653 a| Random Forest;

653 a| Software Defect Prediction;

653 a| Logistic Regression;

700 1 a| Srinivasan, Madhusudan,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/13711;

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An empirical exploration of artificial intelligence for software defect prediction in software engineering / by Elaine Cahill.

Author/creator	Cahill, Elaine author.
Other author	Srinivasan, Madhusudan, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2024.
Description	1 online resources (89 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Artificial intelligence--Data processing. Machine learning. Software failures--Prevention. --Data processing. Support vector machines. Software engineering. Ensemble learning (Machine learning)

Summary	Artificial Intelligence (AI) is an important topic in software engineering not only for data analysis and pattern recognition, but for the opportunity of finding solutions to problems that may not have explicit rules or instructions. Reliable prediction methods are needed because we cannot prove that there are no defects in software. Deep learning and machine learning have been applied to software defect prediction in the attempt to generate valid software engineering practices since at least 1971. Avoiding safety-critical or expensive system failures can save lives and reduce the economic burden of maintaining systems by preventing failures in systems such as aviation software, medical devices, and autonomous vehicles. This thesis contributes to the field of software defect prediction by empirically evaluating the performance of various machine learning models, including Logistic Regression, Random Forest, Support Vector Machine, and a stacking classifier combining these models. The findings highlight the importance of model selection and feature engineering in achieving accurate predictions. We followed this with a stacking classifier that combines Logistic Regression, Random Forest, and Support Vector Machine (SVM) to see if that improved predictive performance. We compared our results with previous work and analyzed which features or attributes appeared to be effective in predicting defects. We end by discussing potential next steps for further research based on our work results.
General note	Advisor: Madhusudan Srinivasan
General note	Title from PDF t.p. (viewed December 1, 2025).
Dissertation note	M.S. East Carolina University 2024.
Dissertation note	Presented to the Faculty of the Department of Computer Science
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

An empirical exploration of artificial intelligence for software defect prediction in software engineering / by Elaine Cahill.

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