Datalog-Based Language-Agnostic Change Impact Analysis for Microservices

The shift-left principle in the industry requires us to test a software application as early as possible. In particular, when code changes in a microservice application are committed to the code repository, we have to efficiently identify all public microservice interfaces affected by the changes so...

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Bibliographic Details
Published inProceedings / International Conference on Software Engineering pp. 78 - 89
Main Authors Shi, Qingkai, Xie, Xiaoheng, Fu, Xianjin, Di, Peng, Li, Huawei, Zhou, Ang, Fan, Gang
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.04.2025
Subjects
change impact analysis
Codes
Companies
Computer languages
Datalog-based analysis
Industries
Microscopy
Microservice architectures
microservices
Multilingual
Software
Software engineering
Testing
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Summary:The shift-left principle in the industry requires us to test a software application as early as possible. In particular, when code changes in a microservice application are committed to the code repository, we have to efficiently identify all public microservice interfaces affected by the changes so that the impacted interfaces can be tested as soon as possible. However, developing an efficient change impact analysis is extremely challenging in microservices due to the multilingual problem: microservice applications are often implemented using varying programming languages and involve diverse frameworks and configuration files. To address this issue, this paper presents MICROSCOPE, a language-agnostic change impact analysis that uniformly represents code, configuration files, frameworks, and code changes by relational Datalog rules. MICROSCOPE then benefits from an efficient Datalog solver to identify impacted interfaces. Experiments based on the use of MICROSCOPE in Ant Group, a leading software vendor, demonstrate that MICROSCOPE is both effective and fast, as it successfully identifies interfaces affected by 112 code commits, with moderate time overhead, and could reduce 97% of interfaces to test and save 73% of testing time after code changes.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00115