NDLI: REMI: defect prediction for efficient API testing

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REMI: defect prediction for efficient API testing

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REMI: defect prediction for efficient API testing

Content Provider	ACM Digital Library
Author	Kim, Mijung Choi, Soonhwang Nam, Jaechang Yeon, Jaehyuk Kim, Sunghun
Abstract	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. .
Starting Page	990
Ending Page	993
Page Count	4
File Format	PDF
ISBN	9781450336758
DOI	10.1145/2786805.2804429
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Quality assurance Api testing Defect prediction
Content Type	Text
Resource Type	Article

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