Quality assessment of crowdsourced test cases

Abstract

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 quality-assessment techniques. Moreover, TCQA provided yield real-time insights on test-case quality before the assessment was finished.