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 blackbox 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 interparameter dependencies), as these cannot be formally described in current API specification languages. As a result, when testing real-world services, most random test cases tend to be invalid since they violate some of the inter-parameter dependencies of the service, making human intervention indispensable. 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. The model is trained with the API requests and responses collected during the generation and execution of previous test cases. Preliminary results with five real-world RESTful APIs and 16K automatically generated test cases show that test inputs validity can be predicted with an accuracy ranging from 86% to 100% in APIs like Yelp, GitHub, and YouTube. These are encouraging results that show the potential of artificial intelligence to improve current test case generation techniques.Index Terms-RESTful web API, web services testing, artificial neural network
Examined the nature of changes in abilities contributing to individual differences in a criterion task (auditory signal identification) when 2 task characteristics (signal duration and signal-to-noise ratio) were systematically varied. 127 male college students performed under 9 different conditions on the criterion task (3-, 6-, or 9-sec signal durations, and -5 db, 0 db, and 5 db signal-to-noise ratios), as well as on a battery of 24 printed and auditory ability measures (e.g., Object-Number, Gestalt Completion, and Hidden Tunes tests). Variations in the 2 task characteristics were found to increase task difficulty. Of the 6 ability factors identified, one (Auditory Perceptual) predicted criterion task performance, and this prediction increased systematically as each manipulation increased the task's difficulty. It was possible to specify the task characteristics under which the ability was least critical and most critical to criterion performance. Implications of the results for linking ability requirements and task characteristics are discussed. (29 ref)
One aspect of the sensory interaction phenomenon was reviewed, the effect of ambient noise upon signal detection performance. An objective of this review was to arrive at possible generalizations about the effects of noise through an examination of variables affecting both similarities and divergencies of results. A second objective was to discuss some of the limitations of noise research for theory and practice, using sonar surveillance in undersea warfare as a reference operation. The conclusions from the review were organized under two major headings:(I) effects of noise for the alerted operator case involving threshold sensitivity, and (2) effects of noise for the unalerted operator case involving vigilance behavior. The limitations of the literature for theory and practice were also discussed under these major headings.
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