REMI: Defect Prediction for Efficient API Testing

Mijung Kim, Jaechang Nam, Jaehyuk Yeon, Soonhwang Choi, and Sunghun Kim. "REMI: Defect Prediction for Efficient API Testing." ESEC/FSE 2015. 990-993.

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Quality assurance for common APIs is important since the the reliability of APIs affects the quality of other systems using the APIs. Testing is a common practice to ensure the quality of APIs, but it is a challenging and laborious task especially for industrial projects. Due to a large number of APIs with tight time constraints and limited resources, it is hard to write enough test cases for all APIs. To address these challenges, we present a novel technique, REMI that predicts high risk APIs in terms of producing potential bugs. REMI allows developers to write more test cases for the high risk APIs. We evaluate REMI on a real-world industrial project, Tizen-wearable, and apply REMI to the API development process at Samsung Electronics. Our evaluation results show that REMI predicts the bug-prone APIs with reasonable accuracy (0.681 f-measure on average). The results also show that applying REMI to the Tizen-wearable development process increases the number of bugs detected, and reduces the resources required for executing test cases.