We work on tools, languages and methodologies to dramatically increase the productivity of software development. We are interested in both analysis tools for existing software, as well as in asking questions about how software of the future should be designed. The following is a preview of the research areas that we are currently working on: Verification and Testing, Probabilistic Programming and Scalable Distributed Systems.
Verification and Testing
Ensuring correctness of programs is crucial for software development. The focus of this research is to build tools and techniques to ensure that a program meets its specification.
The Q program verifier brings together state-of-the-art tools to enable automated verification of programs written in a variety of programming languages, leveraging symbolic search techniques. Q stands out from its competition in its unique support for analyzing concurrent, multi-core, as well as distributed systems. The Yogi project pioneered the art of combining static analysis with testing in order to prove safety properties of sequential programs. Seal is a research prototype that infers and summarizes the potential side-effects of a method in a C# program. It is scalable, capable of consuming entire .NET libraries, and supports sophisticated C# features such as LINQ, delegates, event-handler, exceptions and so on. We also work on the application of machine learning techniques for efficient and scalable program verification.
- Alex Aiken, Aditya V. Nori and Rahul Sharma. Bias-Variance Tradeoffs in Program Analysis. In POPL '14: Principles of Programming Languages, January 2014
- Aditya V. Nori and Rahul Sharma. Termination Proofs from Tests. In ESEC-FSE '13: Foundations of Software Engineering, August 2013
- Ravichandhran Madhavan, Ganesan Ramalingam, and Kapil Vaswani, Modular Heap Analysis For Higher Order Programs, in Static Analysis Symposium (SAS), September 2012
- Akash Lal, Shaz Qadeer, and Shuvendu Lahiri, Corral: A Solver for Reachability Modulo Theories, in Computer-Aided Verification (CAV), July 2012
- Patrice Godefroid, Aditya V. Nori, Sriram K. Rajamani, and Sai Deep Tetali. Compositional May-Must Program Analysis: Unleashing the Power of Alternation. In POPL '10: Principles of Programming Languages, January 2010
Scalable Distributed Systems
Building systems that can scale seamlessly under load is an art. Scalability is a concern that pervades every aspect of the system stack including languages, runtimes, and storage. Unfortunately, scalability conflicts with other desirable properties such as consistency and security. The focus of this research is to rethink programming models, storage systems, and tools with scalability as the primary goal.
The CScale project explores new designs of distributed databases. As part of this project, we have developed a new replication protocol for building consistent and resilient distributed data structures. With Cipherbase, we are rethinking the design of databases and database abstractions with security as a first class citizen. We are building a compiler that securely migrates existing database applications to public cloud platforms. Perforator is a tool for costing and optimizing queries running on distributed query engines such as HIVE and DryadLINQ.
Jose Faleiro, Sriram Rajamani, Kaushik Rajan, G Ramalingam, and Kapil Vaswani, Generalized Lattice Agreement, in Principles of Distributed Computing (PODC), ACM, July 2012
G Ramalingam and Kapil Vaswani, Fault Tolerance via Idempotence, in Principles of Programming Languages (POPL), ACM, January 2013
Probabilistic programs are usual programs with two added constructs: (1) the ability to draw values at random from distributions, and (2) the ability to condition values of variables in a program through observations. Models from diverse application areas such as computer vision, coding theory, cryptographic protocols, biology and reliability analysis can be written as probabilistic programs.
Probabilistic inference is the problem of computing an explicit representation of the probability distribution implicitly specified by a probabilistic program. We show connections this research area has with programming languages and software engineering, including language design, static and dynamic analysis. These ideas form the basis of the tool R2.
Guillaume Claret, Sriram K. Rajamani, Aditya V. Nori, Andrew D. Gordon, and Johannes Borgstrom, Bayesian Inference using Data Flow Analysis, in Foundations of Software Engineering (FSE), ACM, August 2013
Arun T. Chaganty, Aditya V. Nori, Sriram K. Rajamani. Efficiently Sampling Probabilistic Programs via Program Analysis. In AISTATS '13: Artificial Intelligence and Statistics, April 2013
Andrew D. Gordon, Mihhail Aizatulin, Johannes Borgström, Guillaume Claret, Thore Graepel, Aditya V. Nori, Sriram K. Rajamani, and Claudio Russo. A Model-Learner Pattern for Bayesian Reasoning. In POPL '13: Principles of Programming Languages, January 2013
- We are looking for graduate interns for summer'14. Please apply here and write to us directly at email@example.com
- We are hiring, please check out the careers page
- The Q program verifier will replace SLAM in the next version of the Static Driver Verifier!
- Tom Ball and Sriram Rajamani are recipients of the 2011 CAV award!
- Automatic Predicate Abstraction of C Programs wins the Most Influential PLDI Paper award!