TY  - EJOU
AU  - Ramzan, Hafiz Arslan 
AU  - Islam, Kamrul 
AU  - Hussain, Md Ahbab 
AU  - Monim, Raiyan Muntasir 
AU  - Asad, Sabit Md 
AU  - Ramzan, Sadia 

TI  - Data-Driven Test Case Prioritization (DD-TCP): A Machine Learning Framework for Intelligent Software Quality Assurance
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Regression testing of large-scale, data-intensive software systems demands efficient test-case prioritization strategies to detect faults early while minimizing computational cost. Conventional prioritization methods, such as coverage-based and risk-based approaches, lack adaptability to evolving project dynamics and fail to leverage the rich test-execution data accumulated over continuous integration cycles. This study presents a Data-Driven Test-Case Prioritization (DD-TCP) Framework that incorporates statistical and machine-learning techniques to model the relationship between test-case features and historical fault detection outcomes. The framework extracts multidimensional attributes including code-change frequency, dependency metrics, execution duration, and past failure density, which are normalized and embedded into a predictive ranking model based on gradient-boosted decision trees. Test cases are then dynamically reordered using a probabilistic gain function that maximizes early fault detection probability. Comprehensive simulations on representative open-source project datasets and synthetically generated large-scale test suites reveal that the proposed Data-Driven Test-Case Prioritization (DD-TCP) framework consistently achieves superior performance, yielding a 32.4% improvement in Average Percentage of Faults Detected (APFD) and a 27.1% reduction in execution overhead relative to baseline methods. The results demonstrate the feasibility of data-centric intelligence for scalable regression testing and provide an analytical foundation for integrating machine learning into next-generation Software Quality Assurance pipelines.
KW  - Data-driven test-case prioritization; regression testing; software quality assurance; machine learning; continuous integration; fault detection efficiency; intelligent software systems

DO  - 10.32604/cmc.2026.077782