An Abstract Domain to Infer Symbolic Ranges over Nonnegative Parameters

AbstractThe value range information of program variables is useful in many applications such as compiler optimization and program analysis. In the framework of abstract interpretation, the interval abstract domain infers numerical bounds for each program variable. However, in certain applications such as automatic parallelization, symbolic ranges are often desired. In this paper, we present a new numerical abstract domain, namely the abstract domain of parametric ranges, to infer symbolic ranges over nonnegative parameters for each program variable. The new domain is designed based on the insight that in certain contexts, program procedures often have nonnegative parameters, such as the length of an input list and the size of an input array. The domain of parametric ranges seeks to infer the lower and upper bounds for each program variable where each bound is a linear expression over nonnegative parameters. The time and memory complexity of the domain operations of parametric ranges is O(nm) where n is the number of program variables and m is the number of nonnegative parameters. On this basis, we show the application of parametric ranges to infer symbolic ranges of the sizes of list segments in programs manipulating singly-linked lists. Finally, we show preliminary experimental results

Range Analysis of Binaries with Minimal Effort

COTS components are ubiquitous in military, industrial and governmental systems. However, the bene?fits of reduced development and maintainance costs are compromised by security concerns. Since source code is unavailable, security audits necessarily occur at the binary level. Push-button formal method techniques, such as model checking and abstract interpretation, can support this process by, among other things, inferring ranges of values for registers. Ranges aid the security engineer in checking for vulnerabilities that relate, for example, to integer wrapping, uninitialised variables and bu?er over ows. Yet the lack of structure in binaries limits the e?ffectiveness of classical range analyses based on widening. This paper thus contributes a simple but novel range analysis, formulated in terms of linear programming, which calculates ranges without manual intervention

Range and Set Abstraction using SAT

Symbolic decision trees are not the only way to correlate the relationship between flags and numeric variables. Boolean formulae can also represent such relationships where the integer variables are modelled with bit-vectors of propositional variables. Boolean formulae can be composed to express the semantics of a block and program state, but they are hardly tractable, hence the need to compute their abstractions. This paper shows how incremental SAT can be applied to derive range and set abstractions for bit-vectors that are constrained by Boolean formulae

Memory Usage Inference for Object-Oriented Programs

We present a type-based approach to statically derive symbolic closed-form formulae that characterize the bounds of heap memory usages of programs written in object-oriented languages. Given a program with size and alias annotations, our inference system will compute the amount of memory required by the methods to execute successfully as well as the amount of memory released when methods return. The obtained analysis results are useful for networked devices with limited computational resources as well as embedded software.Singapore-MIT Alliance (SMA

Integrated Reasoning and Proof Choice Point Selection in the Jahob System – Mechanisms for Program Survival

In recent years researchers have developed a wide range of powerful automated reasoning systems. We have leveraged these systems to build Jahob, a program specification, analysis, and verification system. In contrast to many such systems, which use a monolithic reasoning approach, Jahob provides a general integrated reasoning framework, which enables multiple automated reasoning systems to work together to prove the desired program correctness properties. We have used Jahob to prove the full functional correctness of a collection of linked data structure implementations. The automated reasoning systems are able to automatically perform the vast majority of the reasoning steps required for this verification. But there are some complex verification conditions that they fail to prove. We have therefore developed a proof language, integrated into the underlying imperative Java programming language, that developers can use to control key choice points in the proof search space. Once the developer has resolved these choice points, the automated reasoning systems are able to complete the verification. This approach appropriately leverages both the developer’s insight into the high-level structure of the proof and the ability of the automated reasoning systems to perform the mechanical steps required to prove the verification conditions. Building on Jahob’s success with this challenging program verification problem, we contemplate the possibility of verifying the complete absence of fatal errors in large software systems. We envision combining simple techniques that analyze the vast majority of the program with heavyweight techniques that analyze those more sophisticated parts of the program that may require arbitrarily sophisticated reasoning. Modularity mechanisms such as abstract data types enable the sound division of the program for this purpose. The goal is not a completely correct program, but a program that can survive any remaining errors to continue to provide acceptable service

Failure-Oblivious Computing and Boundless Memory Blocks

Memory errors are a common cause of incorrect software execution and security vulnerabilities. We have developed two new techniques that help software continue to execute successfully through memory errors: failure-oblivious computing and boundless memory blocks. The foundation of both techniques is a compiler that generates code that checks accesses via pointers to detect out of bounds accesses. Instead of terminating or throwing an exception, the generated code takes another action that keeps the program executing without memory corruption. Failure-oblivious code simply discards invalid writes and manufactures values to return for invalid reads, enabling the program to continue its normal execution path. Code that implements boundless memory blocks stores invalid writes away in a hash table to return as the values for corresponding out of bounds reads. he net effect is to (conceptually) give each allocated memory block unbounded size and to eliminate out of bounds accesses as a programming error. We have implemented both techniques and acquired several widely used open source servers (Apache, Sendmail, Pine, Mutt, and Midnight Commander).With standard compilers, all of these servers are vulnerable to buffer overflow attacks as documented at security tracking web sites. Both failure-oblivious computing and boundless memory blocks eliminate these security vulnerabilities (as well as other memory errors). Our results show that our compiler enables the servers to execute successfully through buffer overflow attacks to continue to correctly service user requests without security vulnerabilities.Singapore-MIT Alliance (SMA

Beyond reachability: Shape abstraction in the presence of pointer arithmetic

Abstract. Previous shape analysis algorithms use a memory model where the heap is composed of discrete nodes that can be accessed only via access paths built from variables and field names, an assumption that is violated by pointer arithmetic. In this paper we show how this assumption can be removed, and pointer arithmetic embraced, by using an analysis based on separation logic. We describe an abstract domain whose elements are certain separation logic formulae, and an abstraction mechanism that automatically transits between a low-level RAM view of memory and a higher, fictional, view that abstracts from the representation of nodes and multiword linked-lists as certain configurations of the RAM. A widening operator is used to accelerate the analysis. We report experimental results obtained from running our analysis on a number of classic algorithms for dynamic memory management.

Transfer Function Synthesis without Quantifier Elimination

Traditionally, transfer functions have been designed manually for each operation in a program, instruction by instruction. In such a setting, a transfer function describes the semantics of a single instruction, detailing how a given abstract input state is mapped to an abstract output state. The net effect of a sequence of instructions, a basic block, can then be calculated by composing the transfer functions of the constituent instructions. However, precision can be improved by applying a single transfer function that captures the semantics of the block as a whole. Since blocks are program-dependent, this approach necessitates automation. There has thus been growing interest in computing transfer functions automatically, most notably using techniques based on quantifier elimination. Although conceptually elegant, quantifier elimination inevitably induces a computational bottleneck, which limits the applicability of these methods to small blocks. This paper contributes a method for calculating transfer functions that finesses quantifier elimination altogether, and can thus be seen as a response to this problem. The practicality of the method is demonstrated by generating transfer functions for input and output states that are described by linear template constraints, which include intervals and octagons.Comment: 37 pages, extended version of ESOP 2011 pape