Web Application Programming Interfaces (APIs) allow services to be accessed over the network. RESTful (or REST) APIs, which use the REpresentation State Transfer (REST) protocol, are a popular type of web API. To use or test REST APIs, developers use specifications written in standards such as OpenAPI. However, creating and maintaining these specifications is time-consuming and error-prone, especially as software evolves, leading to incomplete or inconsistent specifications that negatively affect the use and testing of the APIs. To address this problem, we present Respector (REST API specification generator), the first technique to employ static and symbolic program analysis to generate specifications for REST APIs from their source code. We evaluated Respector on 15 real-world APIs with promising results in terms of precision and recall in inferring endpoint methods, endpoint parameters, method responses, and parameter attributes, including constraints leading to successful HTTP responses or errors. Furthermore, these results could be further improved with additional engineering. Comparing the Respector-generated specifications with the developer-provided ones shows that Respector was able to identify many missing end-point methods, parameters, constraints, and responses, along with some inconsistencies between developer-provided specifications and API implementations. Finally, Respector outperformed several techniques that infer specifications from annotations within API implementations or by invoking the APIs.

Massive volumes of uncertain trajectory data are being generated by GPS devices. Due to the limitations of GPS data, these trajectories are generally uncertain. This state of affairs renders it is attractive to be able to compress uncertain trajectories and to be able to query the trajectories efficiently without the need for (full) decompression. Unlike existing studies that target accurate trajectories, we propose a framework that accommodates uncertain trajectories in road networks. To address the large cardinality of instances of a single uncertain trajectory, we exploit the similarity between uncertain trajectory instances and provide a referential representation. First, we propose a reference selection algorithm based on the notion of Fine-grained Jaccard Distance to efficiently select trajectory instances as references. Then we provide referential representations of the different types of information contained in trajectories to achieve high compression ratios. In particular, a new compression scheme for temporal information is presented to take into account variations in sample intervals. Finally, we propose an index and develop filtering techniques to support efficient queries over compressed uncertain trajectories. Extensive experiments with real-life datasets offer insight into the properties of the framework and suggest that it is capable of outperforming the existing state-of-the-art method in terms of both compression ratio and efficiency.