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003 OCoLC;

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040 a| EREb| enge| rdac| ERE;

035 a| (OCoLC)1523697849;

049 a| EREE;

100 1 a| Guzman-Sanchez, Josee| author?| UNAUTHORIZED;

245 10 a| Risk-Based Test Case Prioritization Using Large Language Models in Regression Testing;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2025.;

300 a| 46 pages;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 793.72 KB2| rda;

538 a| System requirements: Adobe Reader.;

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

502 c| East Carolina Universityd| 2025.;

504 a| Includes bibliographical references.;

520 3 a| Regression testing is critical to ensuring software quality after performing code modifica- tions. However, complete test execution on complex and robust test suites can be infeasible due to time and resource constraints. Therefore, test case prioritization (TCP) strategies aim to organize test cases to increase fault detection rates early during test execution. This study proposes a risk-based test case prioritization approach that leverages large language models (LLMs) to estimate the fault-proneness of individual methods to guide the prior- itization process. An LLM is fine-tuned to predict the risk score of each function based on several software metrics, which is used to perform static analysis of test cases to de- termine an overall risk ranking. The prioritized test suites are evaluated using established metrics, including Fault Detection Rate (FDR) and Average Percentage of Faults Detected (APFD). The evaluation of this approach is compared against baseline techniques such as coverage-based and randomized prioritization. The results of this experiment, conducted on open-source Java projects, determined that the risk-based LLM prioritization approach outperforms traditional TCP methods in early fault detection, highlighting the potential of including LLMs in regression testing workflows.;

653 a| Regression testing;

653 a| Large language models;

653 a| Test case prioritization;

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

710 2 a| East Carolina University=| ^A637090;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/14048;

949 a| Click on web addressw| ASISh| JOYNER101;

949 a| Click on web addressw| ASISh| HSL111;

994 a| C0b| ERE;

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998 a| 6584133;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6584133-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 6/16/2025x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6584133-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 6/16/2025x| ETDz| HERESOURCE;

Risk-Based Test Case Prioritization Using Large Language Models in Regression Testing

Author/creator	Guzman-Sanchez, Jose author
Other author	Srinivasan, Madhusudan degree supervisor.
Other author	East Carolina University
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2025.
Description	46 pages
Supplemental Content	Access via ScholarShip

Summary	Regression testing is critical to ensuring software quality after performing code modifica- tions. However, complete test execution on complex and robust test suites can be infeasible due to time and resource constraints. Therefore, test case prioritization (TCP) strategies aim to organize test cases to increase fault detection rates early during test execution. This study proposes a risk-based test case prioritization approach that leverages large language models (LLMs) to estimate the fault-proneness of individual methods to guide the prior- itization process. An LLM is fine-tuned to predict the risk score of each function based on several software metrics, which is used to perform static analysis of test cases to de- termine an overall risk ranking. The prioritized test suites are evaluated using established metrics, including Fault Detection Rate (FDR) and Average Percentage of Faults Detected (APFD). The evaluation of this approach is compared against baseline techniques such as coverage-based and randomized prioritization. The results of this experiment, conducted on open-source Java projects, determined that the risk-based LLM prioritization approach outperforms traditional TCP methods in early fault detection, highlighting the potential of including LLMs in regression testing workflows.
Dissertation note	East Carolina University 2025.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Risk-Based Test Case Prioritization Using Large Language Models in Regression Testing

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