Various software-engineering problems have been solved by crowdsourcing.In many projects,the software outsourcing process is streamlined on cloud-based platforms.Among software engineering tasks,test-case development is particularly suitable for crowdsourcing,because a large number of test cases can be generated at little monetary cost.However,the numerous test cases harvested from crowdsourcing can be high-or low-quality.Owing to the large volume,distinguishing the high-quality tests by traditional techniques is computationally expensive.Therefore,crowdsourced testing would benefit from an efficient mechanism distinguishes the qualities of the test cases.This paper introduces an automated approach-TCQA-to evaluate the quality of test cases based on the onsite coding history.Quality assessment by TCQA proceeds through three steps: (1) modeling the code history as a time series,(2) extracting the multiple relevant features from the time series,and (3) building a model that classifies the test cases based on their qualities.Step (3) is accomplished by feature-based machine-learning techniques.By leveraging the onsite coding history,TCQA can assess the test-case quality without performing expensive source-code analysis or executing the test cases.Using the data of nine test-development tasks involving more than 400 participants,we evaluated TCQA from multiple perspectives.The TCQA approach assessed the quality of the test cases with higher precision,faster speed,and lower overhead than conventional test-case qualityassessment techniques.Moreover,TCQA provided yield real-time insights on test-case quality before the assessment was finished.