Mutation Testing of Event Processing Queries
Bibliographic record
Abstract
Event processing queries are intended to process continuous event streams. These queries are partially similar to traditional SQL queries, but provide the facilities to express rich features (e.g., pattern expression, sliding window of length and time). An error while implementing a query may result in abnormal program behaviors and lost business opportunities. Moreover, queries can be generated with unsanitized inputs and the structure of intended queries might be altered. Thus, a tester needs to test the behavior of queries in presence of malicious inputs. Mutation testing has been found to be effective to assess test suites quality and generating new test cases. Unfortunately, there is no effort to perform mutation testing of event processing queries. In this work, we propose mutation-based testing of event processing queries. We choose Event Processing Language (EPL) as our case study and develop necessary mutation operators and killing criteria to generate high quality event streams and malicious inputs. Our proposed operators modify different features of EPL queries (pattern expression, windows of length and time, batch processing of events). We develop an architecture to generate mutants for EPL and perform mutation analysis. We evaluate our proposed EPL mutation testing approach with a set of developed benchmark containing diverse types EPL queries. The evaluation results indicate that the proposed operators and mutant killing criteria are effective to generate test cases capable of revealing anomalous program behaviors (e.g., event notification failure, delay of event reporting, unexpected event), and SQL injection attacks. Moreover, the approach incurs less manual effort and can complement other testing approach such as random testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".