Abstract flow learning for web application test generation

摘要: Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and cases, the development scripts to support automated regression testing. To keep pace with evolution, artifacts must also be frequently updated. Although automation practices help mitigate cost testing, a large gap exists between current paradigm fully Researchers practitioners are realizing potential for artificial intelligence machine learning (ML) bridge capabilities humans those machines. This paper presents an ML approach that combines language specification includes grammar can used describe flows, trainable flow generation model, in order generate tests way is trainable, reusable across different applications, generalizable new applications.