Detecting Logic Bugs in Graph Database Management Systems via Injective and Surjective Graph Pattern Transformation
Graph Database Management Systems (GDBMSs) store data as graphs and have several important use cases, such as social networks and recommendation systems. However, they can be affected by logic bugs, which cause the GDBMSs to compute incorrect results and subsequently affect the applications relying on them. In this work, we propose injective and surjective Graph Pattern Transformation (GPT) to detect logic bugs in GDBMSs. Given a query 𝑄, we derive a mutated query 𝑄′, so that either their result sets are: (i) semantically equivalent; or (ii) variant based on the mutation to be either a subset or superset of each other. When the expected relationship between the results does not hold, a logic bug in the GDBMS is detected. The key insight to mutate 𝑄 is that the graph pattern in graph queries enables systematic query transformations derived from injective and surjective mappings of the directed edge sets between 𝑄 and 𝑄′. We implemented injective and surjective Graph Pattern Transformation (GPT) as a tool called GraphGenie and evaluated it on 6 popular and mature GDBMSs. GraphGenie has found 25 unknown bugs including 16 logic bugs, 3 internal errors, and 6 performance issues, where 12 bugs have been fixed by vendors and 9 confirmed. Our results demonstrate the practicality and effectiveness of GraphGenie in detecting logic bugs in GDBMSs and the potential for improving the reliability of applications relying on these GDBMSs
Thu 18 AprDisplayed time zone: Lisbon change
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