Code Quality Optimization in Salesforce Using Predictive Static Analysis Models
DOI:
https://doi.org/10.64137/3107-9458/ICACSIS-103Keywords:
Salesforce, Apex, Lwc, Code Quality, Static Analysis, Predictive Models, Machine Learning, Technical Debt, Ci/Cd, Software EngineeringAbstract
Due to its metadata-driven structure, the interaction of declarative and programmatic components, and many exceptions to code quality that standard rule-based static analysis methods cannot properly recognize, the development of Salesforce is a very complex problem. This paper presents new models of predictive static analysis that are more successful in localizing defects and quality degradation. These models incorporate machine learning techniques along with Salesforce-specific code metrics. The authors of the paper were thus able to generate a dataset of Apex classes, triggers, and Lightning components, gather metrics such as cyclomatic complexity, SOQL/DML density, governor-limit sensitivity, and structural patterns, and then train and test models like logistic regression, random forests, and gradient boosting. The case study clearly demonstrates that these prediction models lead to defect identification that is significantly more efficient, a reduction of technical debt, and better compliance with Salesforce best practices compared to the results of traditional static analysis. The findings emphasize the value of implementing AI-driven quality control management as a highly valuable resource in the Salesforce DevOps pipeline and also indicate that there are numerous possibilities for the future of automation beyond the current state, such as code embeddings, real-time anomaly detection, and Salesforce DX-integrated remediation workflows.
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