A Systematic Review of Combinatorial Testing Strategies for Cross-Border E-Commerce Platforms: Methods, Architectures, and Future Research Directions
Bibliographic record
Abstract
Cross-border e-commerce platforms have significantly transformed global trade by enabling seamless transactions between buyers and sellers across diverse geographical regions. These platforms operate in complex environments characterized by varied user preferences, multiple payment gateways, diverse regulatory requirements, and multilingual interfaces, making reliability, scalability, and correctness critical challenges. Combinatorial testing has emerged as an effective approach to systematically evaluate interactions among multiple input parameters, reducing testing effort while maintaining high fault detection capability. This review examines combinatorial testing strategies applied to cross-border e-commerce systems, focusing on testing methods, architectural considerations, and emerging research trends. Techniques such as pairwise testing, t-way testing, constraint-based testing, and adaptive combinatorial testing are analyzed alongside modern architectures including microservices, cloud-based infrastructures, and distributed systems. The study highlights trends such as the integration of artificial intelligence, automated test generation, and optimization methods for large-scale applications. Despite its advantages, challenges related to handling complex constraints, scalability, and real-time testing persist. The findings underscore the need for hybrid testing strategies, enhanced tool support, and the incorporation of machine learning techniques to improve testing efficiency, with future directions emphasizing intelligent and adaptive testing frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".