Deep Learning Based Prediction Of Test Input Validity For Restful Apis Automated test case generation for restful web apis is a thriving research topic due to their key role in software integration. most approaches in this domain follow a black box approach, where test cases are randomly derived from the api specification. these techniques show promising results, but they neglect constraints among input parameters (so called inter parameter dependencies), as. In this paper, we proposed a deep learning based approach for predicting the validity of test inputs for restful apis. starting from a dataset of previous calls labeled as valid or faulty, the proposed network is able to predict the validity of new api calls with an accuracy of 97%.
Pdf Deep Learning Based Prediction Of Test Input Validity For Restful In this paper, we propose a deep learning based approach for automatically predicting the validity of an api request (i.e., test input) before calling the actual api. Mirabella et al. [56] generated automated test cases for testing restful web apis and proposed a deep learning based approach to predict the validity of an api request before responding to it. This paper introduces deeprest, a novel black box approach for automatically testing rest apis. it leverages deep reinforcement learning to uncover implicit api constraints, that is, constraints hidden from api documentation. curiosity driven learning guides an agent in the exploration of the api and learns an efective or der to test its. Deep learning based prediction of test input validity for restful apis. in international workshop on deep learning for testing and testing for deep learning. 9 16.
Figure 1 From Deep Learning Based Prediction Of Test Input Validity For This paper introduces deeprest, a novel black box approach for automatically testing rest apis. it leverages deep reinforcement learning to uncover implicit api constraints, that is, constraints hidden from api documentation. curiosity driven learning guides an agent in the exploration of the api and learns an efective or der to test its. Deep learning based prediction of test input validity for restful apis. in international workshop on deep learning for testing and testing for deep learning. 9 16. 1. what are the contributions in "deep learning based prediction of test input validity for restful apis" ? most approaches in this domain follow a blackbox approach, where test cases are randomly derived from the api specification in this paper, the authors propose a deep learning based approach for automatically predicting the validity of an api request ( i. e., test input ) before calling. Deep learning based prediction of test input validity for restful apis. in ieee acm 3rd international workshop on deep learning for testing and testing for deep learning (deeptest’21).
Table I From Deep Learning Based Prediction Of Test Input Validity For 1. what are the contributions in "deep learning based prediction of test input validity for restful apis" ? most approaches in this domain follow a blackbox approach, where test cases are randomly derived from the api specification in this paper, the authors propose a deep learning based approach for automatically predicting the validity of an api request ( i. e., test input ) before calling. Deep learning based prediction of test input validity for restful apis. in ieee acm 3rd international workshop on deep learning for testing and testing for deep learning (deeptest’21).
Comparison And Validation Of Deep Learning Models For The Pdf