Correctness of SQL queries is usually tested by executing the queries on one or more datasets. Erroneous queries are often the results of small changes or mutations of the correct query. A mutation Q' of a query Q is killed by a dataset D if Q(D) = Q'(D). Earlier work on the XData system showed how to generate datasets that kill all mutations in a class of mutations that included join type and comparison operation mutations.In this paper, we extend the XData data generation techniques to handle a wider variety of SQL queries and a much larger class of mutations. We have also built a system for grading SQL queries using the datasets generated by XData. We present a study of the effectiveness of the datasets generated by the extended XData approach, using a variety of queries including queries submitted by students as part of a database course. We show that the XData datasets outperform predefined datasets as well as manual grading done earlier by teaching assistants, while also avoiding the drudgery of manual correction. Thus, we believe that our techniques will be of great value to database course instructors and TAs, particularly to those of MOOCs. It will also be valuable to database application developers and testers for testing SQL queries.
This paper lays the ground work for assistive navigation using wearable sensors and social sensors to foster situational awareness for the blind. Our system acquires social media messages to gauge the relevant aspects of an event and to create alerts. We propose social semantics that captures the parameters required for querying and reasoning an eventof-interest, such as what, where, who, when, severity, and action from the Internet of things, using an event summarization algorithm. Our approach integrates wearable sensors in the physical world to estimate user location based on metric and landmark localization. Streaming data from the cyber world are employed to provide awareness by summarizing the events around the user based on the situation awareness factor. It is illustrated using disaster and socialization event scenarios. Discovered local events are fed back using sound localization so that the user can actively participate in a social event or get early warning of any hazardous events. A feasibility evaluation of our proposed algorithm included comparing the output of the algorithm to ground truth, a survey with sighted participants about the algorithm output, and a sound localization user interface study with blind-folded sighted participants. Thus, our framework supports the navigation problem for the blind by combining the advantages of our real-time localization technologies so that the user is being made aware of the world, a necessity for independent travel.
SQL queries are usually tested for correctness by executing them on one or more datasets, to see if they give the desired results on each dataset. Erroneous queries are often the result of small changes, or mutations, of the correct query. Earlier work on the XData system showed how to generate datasets that kill all mutations in a class of mutations that included join type and comparison operation mutations. However, the system could not handle a number of commonly used SQL features.In this paper we extend the XData data generation techniques to handle features such as null values, string constraints, aggregation with constraints on aggregation results, and a class of subqueries, amongst others. We present a study of the effectiveness of our data generation approach for correcting student SQL assignments that were part of a database course. The datasets generated by XData outperform publicly available datasets, as well as manual grading done earlier by teaching assistants.
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