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K. V. Maheshwara Reddy

dblp:154/6710 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Software testing · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%
Databases, data mining, and information retrieval
1 paper
Data models and query languages · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
test input generation
0.422015
Data generation for testing and grading SQL queries · VLDB J. 2015
The XDa-TA system for automated grading of SQL query assignments · ICDE 2015
Computing education
automated assessment
0.212015
The XDa-TA system for automated grading of SQL query assignments · ICDE 2015
Data models and query languages
SQL
0.212015
Data generation for testing and grading SQL queries · VLDB J. 2015

Methods — techniques the papers use, named apart from their topics

grading · 0.4
YearPublicationVenuePosition
2015 The XDa-TA system for automated grading of SQL query assignments
abstract
Grading of student SQL queries is usually done by executing the query on sample datasets (which may be unable to catch many errors) and/or by manually comparing/checking a student query with the correct query (which can be tedious and error prone). In this demonstration we present the XDa-TA system which can be used by instructors and TAs for grading SQL query assignments automatically. Given one or more correct queries for an SQL assignment, the tool uses the XData system to automatically generate datasets that are designed specifically to catch common errors. The grading is then done by comparing the results of student queries with those of the correct queries against these generated datasets; instructors can optionally provide additional datasets for testing. The tool can also be used in a learning mode by students, where it can provide immediate feedback with hints explaining possible reasons for erroneous output. This tool could be of great value to instructors particularly, to instructors of MOOCs.
Amol Bhangdiya, Bikash Chandra, Biplab Kar, Bharath Radhakrishnan, K. V. Maheshwara Reddy, Shetal Shah, S. Sudarshan 0001
ICDE5
2015 Data generation for testing and grading SQL queries
Bikash Chandra, Bhupesh Chawda, Biplab Kar, K. V. Maheshwara Reddy, Shetal Shah, S. Sudarshan 0001
VLDB J.4