arXiv subjects

P. Madhusudan

Publications and source records attributed to P. Madhusudan.

22 records · Page 2Linked to original sources

arXiv2013

Reachability under Contextual Locking

The pairwise reachability problem for a multi-threaded program asks, given control locations in two threads, whether they can be simultaneously reached in an execution of the program. The problem is important for static analysis and is used to detect statements that are concurrently enabled. This problem is in general undecidable even when data is abstracted and when the threads (with recursion) synchronize only using a finite set of locks. Popular programming paradigms that limit the lock usage patterns have been identified under which the pairwise reachability problem becomes decidable. In this paper, we consider a new natural programming paradigm, called contextual locking, which ties the lock usage to calling patterns in each thread: we assume that locks are released in the same context that they were acquired and that every lock acquired by a thread in a procedure call is released before the procedure returns. Our main result is that the pairwise reachability problem is polynomial-time decidable for this new programming paradigm as well. The problem becomes undecidable if the locks are reentrant; reentrant locking is a \emph{recursive locking} mechanism which allows a thread in a multi-threaded program to acquire the reentrant lock multiple times.

cs.LO↗

arXiv2013

Quantified Data Automata on Skinny Trees: an Abstract Domain for Lists

We propose a new approach to heap analysis through an abstract domain of automata, called automatic shapes. The abstract domain uses a particular kind of automata, called quantified data automata on skinny trees (QSDAs), that allows to define universally quantified properties of singly-linked lists. To ensure convergence of the abstract fixed-point computation, we introduce a sub-class of QSDAs called elastic QSDAs, which also form an abstract domain. We evaluate our approach on several list manipulating programs and we show that the proposed domain is powerful enough to prove a large class of these programs correct.

cs.PL↗

arXiv2013

Learning Universally Quantified Invariants of Linear Data Structures

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 decidable subclass of QDAs, called elastic QDAs, and prove that every QDA has a unique minimally-over-approximating elastic QDA. We then give an application of these theoretically sound and efficient active learning algorithms in a passive learning framework and show that we can efficiently learn quantified linear data structure invariants from samples obtained from dynamic runs for a large class of programs.

cs.PL↗

arXiv2012

Sequentializing Parameterized Programs

We exhibit assertion-preserving (reachability preserving) transformations from parameterized concurrent shared-memory programs, under a k-round scheduling of processes, to sequential programs. The salient feature of the sequential program is that it tracks the local variables of only one thread at any point, and uses only O(k) copies of shared variables (it does not use extra counters, not even one counter to keep track of the number of threads). Sequentialization is achieved using the concept of a linear interface that captures the effect an unbounded block of processes have on the shared state in a k-round schedule. Our transformation utilizes linear interfaces to sequentialize the program, and to ensure the sequential program explores only reachable states and preserves local invariants.

cs.LO↗