Early feature evidence helps triage microservice cross-service changes
Early Prediction of Cross-Service Implementation Scope: Evidence and Limits from Multiple Microservice Projects
Software Engineering
Summary
When software projects use microservices, changes to add new features often affect multiple services. The authors studied how well we can predict if a requested feature will need changes across different services before starting the work. They found that early clues give limited ability to predict cross-service changes, and simple methods work about as well as more complex machine learning models. This means early predictions can help flag likely cross-service changes, but can't accurately estimate the full work needed.
What this means in practice
- •For microservice development teams: Quickly identify feature requests that potentially require changes across multiple services to prioritize coordination efforts.
- •For software project managers: Inform early planning by using lightweight heuristics or models to flag complex cross-service tasks among new features.
Authors
Zhoumaocuo, Xiaozhou Li
Abstract
Microservice architectures aim to localize change, yet feature requests may require implementation across service boundaries. We investigate how well such downstream scope can be anticipated before implementation begins. Using issue--pull-request--commit traceability and implementation evidence corrected through targeted human adjudication, we reconstruct 866 repository-traceable feature requests from 13 open-source microservice projects and map their implementations to validated service boundaries. Cross-service changes occur in 101 requests (11.7%) and exhibit broader implementation footprints, particularly higher code churn, but no clear difference in lead time. Pre-implementation evidence provides limited cross-project predictive signal: the strongest leave-one-system-out model reaches a project-macro Macro-F1 of 0.484, but its advantage over the majority baseline is not significant after Holm correction and is sensitive to small evaluation folds. A local LLM offers no reliable improvement over classical models. Downstream workload is substantially harder to estimate, as learned models do not consistently outperform training-fold median baselines. A simple service-mention heuristic also performs comparably to learned scope triage. These findings show that early feature-request evidence is more useful for lightweight cross-service scope triage than for automated scope determination or precise workload estimation.