CODD: A Dataless Approach to Big Data Testing
Summary: CODD enables dataless testing of Big Data deployments by graphically simulating databases from metadata and platforms, avoiding persistent bulk data. It validates dependencies and scales metadata to target storage or runtime, exposing optimizer/engine bugs and testing plan-bouquet guarantees. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Ashoke S. (Indian Institute of Science)
- 2. Jayant R. Haritsa (Indian Institute of Science)
BibTeX Citation
@article{s_vldb15,
title = {{CODD: A Dataless Approach to Big Data Testing}},
author = {S., Ashoke and Haritsa, Jayant R.},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {12},
pages = {2008--2011},
doi = {10.14778/2824032.2824066},
url = {https://doi.org/10.14778/2824032.2824066},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,037 | HYDRA: A Dynamic Big Data Regenerator | 2018 | VLDB | 5.5025567e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,987 | Plan Bouquets: Query Processing without Selectivity Estimation | 2014 | SIGMOD | 9.3517129e-05 |
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