Correction is all you need: Towards high-order complementary cloud API recommendation correction with abductive reasoning

Abstract

In the cloud era, cloud Application Programming Interfaces (APIs) are the best carriers for service delivery, data exchange, and capability replication. The continuously growing number of cloud APIs in dynamic open networks provides developers with more choices but also overwhelms them with a vast array of options. Existing researches primarily use invocation preferences, search keywords, and quality of service modeling to generate a single function cloud API recommendation list. However, these methods face two problems: (1) In service-oriented software development, developers’ needs for high-order complementary cloud APIs are often overlooked. (2) Current cloud API recommendation methods generate recommendation results in a one-shot manner without further correcting them to enhance performance. To tackle these problems, we propose high-order complementary cloud API recommendation correction with abductive reasoning, named HCCR-CAR. HCCR-CAR comprises of two stages: HCCR and CAR. In HCCR, the complementary scores of candidate cloud APIs is determined by taking into account both the explicit and fine-grained complementary relationships between the cloud API query set and the candidate APIs. Subsequently, the candidate cloud APIs are ranked based on these complementary scores in order to generate high-order complementary recommendation results. In CAR, a reasonable abductive task is designed and an abductive model is utilized to infer the most probable complementary reason for the recommendation results produced by HCCR. By minimizing the abductive loss signal between inferred reason and real reason through back-propagation, the recommendation results are corrected. Experiments are conducted on two real-world cloud API datasets which demonstrate that compared to traditional heuristic recommendation methods and deep learning recommendation methods, HCCR-CAR exhibits superior performance in high-order complementary cloud API recommendation. Furthermore, hyperparameter sensitivity experiments and case analysis validate the effectiveness and practicality of this method. HCCR-CAR is more likely to yield satisfactory results for developers while also ensuring the effectiveness of high-order complementary cloud API recommendation in practical service-oriented software development, thereby effectively enhancing revenue of cloud API providers.