My name is Kia Rahmani and I am a PhD student of computer science at Purdue University. I am a member of the programming languages group, working under the supervision of Professor Suresh Jagannathan and Professor Benjamin Delaware . The main topics of my research interests include formal methods of modeling, analysis and verification of applications. I specifically focus on automated debugging/generation of verifiably correct and scalable distributed database applications.
(1) Sound Detection of Serializability Bugs in Weakly Consistent Database Applications:
In this ongoing project, we present an end-to-end analysis framework for efficient/automated detection and replay of concurrent executions of weakly consistent database applications that diverge from their intended behavior, i.e. manifest a serializability anomaly.
This framework is backed by a static analysis engine which takes a full-fledged java program and compiles it down to an intermediate representation that abstracts away unnecessary details (❶). The abstract representation is then encoded in FOL, allowing us to reduce the problem of constructing a bounded serializability anomaly in the given program to finding a satisfying assignment to a SAT formula. We then explore the space of all solutions to this formula through iterative communications to Z3 (❷,❸) and construct all desired abstract anomalies.
In order to provide users with an easy to grasp notion of bugs and more importantly, to account for low-level specific behaviors of each database system (which can slightly differ from our abstract model of weakly consistent/isolated executions), we also developed a test administration tool. Given the abstract anomalies, our tool maps them back to concrete schedules of operations (❹,❺,❻), sets up the initial state of the database and tries to replay each anomaly by enforcing the specified order between operations (❼) and the possible network partitionings (❽).
(2) Enforcement of fine-grained consistency guarantees using effect orchestration:
Non-deterministic behaviors arise in weakly consistent data stores which can potentially violate application correctness, forcing designers to either implement (very complex) ad-hoc mechanisms to avoid these anomalies, or choose to run applications using stronger levels of consistency than necessary. In this project, we introduced a lightweight runtime verification system that relieves developers from having to make such tradeoffs. We leveraged declarative axiomatic specifications that reflect the necessary constraints any correct implementation must satisfy to guide a runtime consistency enforcement. Experimental results show that the performance of our automatically derived mechanisms is better than both specialized hand-written protocols and common store-offered consistency guarantees.
(3) Coq Implementation of Quelea:
In this project, I formalized and implemented the operational semantics used in the PLDI’15 paper, Declarative Programming over Eventually Consistent Data Stores in the Coq proof assistant. Even though the mere goal of the project was to familiarize myself with proof assistants and formal language definition, I was able to point out numerous previously unknown problems in the paper foe which I offered fixes as well. The Coq implementation and the fixes were later used as a supplementary material for the original paper (source code).
- Fine-grained Distributed Consistency Guarantees with Effect Orchestration
(with Gowtham Kaki and Suresh Jagannathan)
[Workshop on the Principles and Practice of Consistency for Distributed Data (Portugal-April 2018)]
[Tech Report] [Slides]