Explaining Wrong Queries Using Small Examples

Miao, Zhengjie; Roy, Sudeepa; Yang, Jun

doi:10.1145/3299869.3319866

Cited by 25 publications

(19 citation statements)

References 39 publications

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“…A recurring theme in the primary studies, regardless of the research topic, was argumentation for [5,8,101,121] and against [75,88,101] natural learning environments. A natural learning environment better reflects industry, i.e., students' future work environments.…”

Section: Natural and Unnatural Learning Environmentsmentioning

confidence: 99%

SQL Education

Taipalus

Seppänen

2020

ACM Trans. Comput. Educ.

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Structured Query Language (SQL) skills are crucial in software engineering and computer science. However, teaching SQL effectively requires both pedagogical skill and considerable knowledge of the language. Educators and scholars have proposed numerous considerations for the betterment of SQL education, yet these considerations may be too numerous and scattered among different fora for educators to find and internalize, as no systematic mappings or literature reviews regarding SQL education have been conducted. The two main goals of this mapping study are to provide an overview of educational SQL research topics, research types and publication fora, and to collect and propagate SQL teaching practices for educators to utilize. Additionally, we present a short future research agenda based on insights from the mapping process. We conducted a systematic mapping study complemented by snowballing techniques to identify applicable primary studies. We classified the primary studies according to research type, and utilized directed content analysis to classify the primary studies by their topic. Out of our selected 89 primary studies, we identified six recurring topics: (i) student errors in query formulation; (ii) characteristics and presentation of the exercise database; (iii) specific and (iv) non-specific teaching approach suggestions; (v) patterns and visualization; and (vi) easing teacher workload. We list 66 teaching approaches the primary studies argued for (and in some cases against). For researchers, we provide a systematic map of educational SQL research, and future research agenda. For educators, we present an aggregated body of knowledge on teaching practices in SQL education over a time frame of 30 years. In conclusion, we suggest that replication studies, studies on advanced SQL concepts, and studies on aspects other than data retrieval are needed to further educational SQL research.

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Section: Natural and Unnatural Learning Environmentsmentioning

confidence: 99%

SQL Education

Taipalus

Seppänen

2020

ACM Trans. Comput. Educ.

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“…and D ; we then pick the smallest witness overall. If Q 1 and Q 2 are monotone, this approach always yields the smallest counterexample [7]. In [7], we also obtained complexity results for SCP for different classes of queries.…”

Section: Background and Problemmentioning

confidence: 99%

“…If Q 1 and Q 2 are monotone, this approach always yields the smallest counterexample [7]. In [7], we also obtained complexity results for SCP for different classes of queries. For queries involving projection, join, and difference, it is noteworthy that finding the smallest witness for a result tuple is already NP-hard in data complexity, even when the queries are of bounded sizes.…”

Section: Background and Problemmentioning

confidence: 99%

“…In our recent work [7], we solve the problem of finding small counterexamples by building on the work in data provenance and constraint solving. We identify connections to known problems, such as minimal witness , and show why and how to adapt our problem definition for complex cases such as when queries involve aggregation.…”

Section: Introductionmentioning

confidence: 99%

“…We tackle the challenges of finding the smallest counterexample at interactive speed and supporting complex queries involving differences, group-by aggregation, as well as SQL functions and operators in selection and join conditions. A detailed discussion on related work can be found in [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RATest

Miao

Roy

Yang

2019

Proceedings of the 2019 International Conference on Management of Data

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We present a system called RATEST, designed to help debug relational queries against reference queries and test database instances. In many applications, e.g., classroom learning and regression testing, we test the correctness of a user query Q by evaluating it over a test database instance D and comparing its result with that of evaluating a reference (correct) query Q0 over D. If Q(D) differs from Q0(D), the user knows Q is incorrect. However, D can be large (often by design), which makes debugging Q difficult. The key idea behind RATEST is to show the user a much smaller database instance D’ ⊆ D, which we call a counterexample, such that Q(D’) ≠ Q0(D’). RATEST builds on data provenance and constraint solving, and employs a suite of techniques to support, at interactive speed, complex queries involving differences and group-by aggregation. We demonstrate an application of RATEST in learning: it has been used successfully by a large undergraduate database course in a university to help students with a relational algebra assignment.

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