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- Rajeev Alur, Rastislav Bodík, +13 authors Abhishek Udupa
- Dependable Software Systems Engineering
- 2013

—The classical formulation of the program-synthesis problem is to find a program that meets a correctness specification given as a logical formula. Recent work on program synthesis and program optimization illustrates many potential benefits of allowing the user to supplement the logical specification with a syntactic template that constrains the space of… (More)

- Pranav Garg, Daniel Neider, P. Madhusudan, Dan Roth
- POPL
- 2016

Inductive invariants can be robustly synthesized using a learning model where the teacher is a program verifier who instructs the learner through concrete program configurations, classified as positive, negative, and implications. We propose the first learning algorithms in this model with implication counter-examples that are based on machine learning… (More)

- Xiaokang Qiu, Pranav Garg, Andrei Stefanescu, P. Madhusudan
- PLDI
- 2013

We propose natural proofs for reasoning with programs that manipulate data-structures against specifications that describe the structure of the heap, the data stored within it, and separation and framing of sub-structures. Natural proofs are a subclass of proofs that are amenable to completely automated reasoning, that provide sound but incomplete… (More)

- Pranav Garg, Christof Löding, P. Madhusudan, Daniel Neider
- CAV
- 2014

We introduce ICE, a robust learning paradigm for synthesizing invari-ants, that learns using examples, counterexamples , and implications, and show that it admits honest teachers and strongly convergent mechanisms for invariant synthesis. We observe that existing algorithms for black-box abstract interpretation can be interpreted as ICE-learning algorithms.… (More)

- Pranav Garg, P. Madhusudan
- TACAS
- 2011

We show that any concurrent program that is amenable to compositional reasoning can be effectively translated to a sequential program. More precisely, we give a reduction from the verification problem for concurrent programs against safety specifications to the verification of sequential programs against safety specifications, where the reduction is… (More)

Invariant generation lies at the heart of automated program verification , and the learning paradigm for synthesizing invariants is a new promising approach to solve this important problem. Unlike white-box techniques that try to generate an invariant by analyzing the program, learning approaches try to synthesize the invariant given concrete configurations… (More)

- Pranav Garg, Christof Löding, P. Madhusudan, Daniel Neider
- CAV
- 2013

We propose a new automaton model, called quantified data automata over words, that can model quantified invariants over linear data structures, and build poly-time active learning algorithms for them, where the learner is allowed to query the teacher with membership and equivalence queries. In order to express invariants in decidable logics, we invent a… (More)

In industry, software testing and coverage-based metrics are the predominant techniques to check correctness of software. This paper addresses automatic unit test generation for programs written in C/C++. The main idea is to improve the coverage obtained by feedback-directed random test generation methods, by utilizing concolic execution on the generated… (More)

- Rishi Agarwal, Pranav Garg, Josep Torrellas
- 2011 38th Annual International Symposium on…
- 2011

As we move to large manycores, the hardware-based <i>global</i> check-pointing schemes that have been proposed for small shared-memory machines do not scale. Scalability barriers include global operations, work lost to global rollback, and inefficiencies in imbalanced or I/O-intensive loads. Scalable checkpointing requires tracking inter-thread dependences… (More)

- Shambwaditya Saha, Pranav Garg, P. Madhusudan
- CAV
- 2015

We present a technique and an accompanying tool that learns guarded affine functions. In our setting, a teacher starts with a guarded affine function and the learner learns this function using equivalence queries only. In each round, the teacher examines the current hypothesis of the learner and gives a counterexample in terms of an input-output pair where… (More)