Static Analysis for ECMAScript String Manipulation Programs

In recent years, dynamic languages, such as JavaScript or Python, have been increasingly used in a wide range of fields and applications. Their tricky and misunderstood behaviors pose a great challenge for static analysis of these languages. A key aspect of any dynamic language program is the multiple usage of strings, since they can be implicitly converted to another type value, transformed by string-to-code primitives or used to access an object-property. Unfortunately, string analyses for dynamic languages still lack precision and do not take into account some important string features. In this scenario, more precise string analyses become a necessity. The goal of this paper is to place a first step for precisely handling dynamic language string features. In particular, we propose a new abstract domain approximating strings as finite state automata and an abstract interpretation-based static analysis for the most common string manipulating operations provided by the ECMAScript specification. The proposed analysis comes with a prototype static analyzer implementation for an imperative string manipulating language, allowing us to show and evaluate the improved precision of the proposed analysis

Completeness of string analysis for dynamic languages

In Abstract Interpretation, completeness ensures that the analysis does not lose information with respect to the property of interest. In particular, for dynamic languages like JavaScript, completeness of string analysis is a key security issue, as poorly managed string manipulation code may easily lead to significant security flaws. In this paper, we provide a systematic and constructive approach for generating the completion of string domains for dynamic languages, and we apply it to the refinement of existing string abstractions. We also provide an effective procedure to measure the precision improvement obtained when lifting the analysis to complete domains

Static analysis for ECMAscript string manipulation programs

In recent years, dynamic languages, such as JavaScript or Python, have been increasingly used in a wide range of fields and applications. Their tricky and misunderstood behaviors pose a great challenge for static analysis of these languages. A key aspect of any dynamic language program is the multiple usage of strings, since they can be implicitly converted to another type value, transformed by string-to-code primitives or used to access an object-property. Unfortunately, string analyses for dynamic languages still lack precision and do not take into account some important string features. In this scenario, more precise string analyses become a necessity. The goal of this paper is to place a first step for precisely handling dynamic language string features. In particular, we propose a new abstract domain approximating strings as finite state automata and an abstract interpretation-based static analysis for the most common string manipulating operations provided by the ECMAScript specification. The proposed analysis comes with a prototype static analyzer implementation for an imperative string manipulating language, allowing us to show and evaluate the improved precision of the proposed analysis

Warping Cache Simulation of Polyhedral Programs

Techniques to evaluate a program’s cache performance fall into two camps: 1. Traditional trace-based cache simulators precisely account for sophisticated real-world cache models and support arbitrary workloads, but their runtime is proportional to the number of memory accesses performed by the program under analysis. 2. Relying on implicit workload characterizations such as the polyhedral model, analytical approaches often achieve problem-size-independent runtimes, but so far have been limited to idealized cache models. We introduce a hybrid approach, warping cache simulation, that aims to achieve applicability to real-world cache models and problem-size-independent runtimes. As prior analytical approaches, we focus on programs in the polyhedral model, which allows to reason about the sequence of memory accesses analytically. Combining this analytical reasoning with information about the cache behavior obtained from explicit cache simulation allows us to soundly fast-forward the simulation. By this process of warping, we accelerate the simulation so that its cost is often independent of the number of memory accesses

Garbage-Free Abstract Interpretation Through Abstract Reference Counting

Abstract garbage collection is the application of garbage collection to an abstract interpreter. Existing work has shown that abstract garbage collection can improve both the interpreter\u27s precision and performance. Current approaches rely on heuristics to decide when to apply abstract garbage collection. Garbage will build up and impact precision and performance when the collection is applied infrequently, while too frequent applications will bring about their own performance overhead. A balance between these tradeoffs is often difficult to strike. We propose a new approach to cope with the buildup of garbage in the results of an abstract interpreter. Our approach is able to eliminate all garbage, therefore obtaining the maximum precision and performance benefits of abstract garbage collection. At the same time, our approach does not require frequent heap traversals, and therefore adds little to the interpreters\u27s running time. The core of our approach uses reference counting to detect and eliminate garbage as soon as it arises. However, reference counting cannot deal with cycles, and we show that cycles are much more common in an abstract interpreter than in its concrete counterpart. To alleviate this problem, our approach detects cycles and employs reference counting at the level of strongly connected components. While this technique in general works for any system that uses reference counting, we argue that it works particularly well for an abstract interpreter. In fact, we show formally that for the continuation store, where most of the cycles occur, the cycle detection technique only requires O(1) amortized operations per continuation push. We present our approach formally, and provide a proof-of-concept implementation in the Scala-AM framework. We empirically show our approach achieves both the optimal precision and significantly better performance compared to existing approaches to abstract garbage collection

Affine Disjunctive Invariant Generation with Farkas' Lemma

Invariant generation is the classical problem that aims at automated generation of assertions that over-approximates the set of reachable program states in a program. We consider the problem of generating affine invariants over affine while loops (i.e., loops with affine loop guards, conditional branches and assignment statements), and explore the automated generation of disjunctive affine invariants. Disjunctive invariants are an important class of invariants that capture disjunctive features in programs such as multiple phases, transitions between different modes, etc., and are typically more precise than conjunctive invariants over programs with these features. To generate tight affine invariants, existing constraint-solving approaches have investigated the application of Farkas' Lemma to conjunctive affine invariant generation, but none of them considers disjunctive affine invariants

RustHorn: CHC-based Verification for Rust Programs (full version)

Reduction to the satisfiability problem for constrained Horn clauses (CHCs) is a widely studied approach to automated program verification. The current CHC-based methods for pointer-manipulating programs, however, are not very scalable. This paper proposes a novel translation of pointer-manipulating Rust programs into CHCs, which clears away pointers and memories by leveraging ownership. We formalize the translation for a simplified core of Rust and prove its correctness. We have implemented a prototype verifier for a subset of Rust and confirmed the effectiveness of our method.Comment: Full version of the same-titled paper in ESOP202