Symbolic Analysis Tool Research Articles

Attention-based Machine Learning Model for Smart Contract Vulnerability Detection

Ethereum attracts extensive attention due to its distinctive function of smart contract and decentralized applications (Dapps). Since the number of contracts on blockchain has increased vigorously, various security vulnerabilities come up. Researchers rely on static symbolic analysis method at first, and it seems to perform well in the accuracy of vulnerability detection. However, this method requires manual analysis in advance and it needs to traverse all the possible execution paths to find out the vulnerable ones. The deeper the path goes, the more time it costs to detect the contracts. This paper proposes an approach to detect smart contracts vulnerability on blockchain by using machine learning(ML) methods. This approach aims to build a general benchmark for new vulnerability detection in order to reduce the demand of expert manpower. Moreover, the high-speed-performance ML algorithm makes quick detection comes true. As long as we adjust the threshold of the model, it can work as a fast prefilter for the traditional symbolic analysis tools in further improvement of accuracy.

Symbolic Analysis Applied to the Specification of Spatial Trends and Spatial Dependence.

This article provides symbolic analysis tools for specifying spatial econometric models. It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly considers how to econometrically model spatial economic relations that might contain unobserved spatial structure of unknown form. Hypothesis testing is conducted with a symbolic-entropy based non-parametric statistical procedure, recently proposed by Garcia-Cordoba, Matilla-Garcia, and Ruiz (2019), which does not rely on prior weight matrices assumptions. It is shown that the use of geographically restricted semiparametric spatial models is a promising modeling strategy for cross-sectional datasets that are compatible with some types of spatial dependence. The results state that models that merely incorporate space coordinates might be sufficient to capture space dependence. Hedonic models for Baltimore, Boston, and Toledo housing prices datasets are revisited, studied (with the new proposed procedures), and compared with standard spatial econometric methodologies.

JaVerT 2.0: compositional symbolic execution for JavaScript

We propose a novel, unified approach to the development of compositional symbolic execution tools, bridging the gap between classical symbolic execution and compositional program reasoning based on separation logic. Using this approach, we build JaVerT 2.0, a symbolic analysis tool for JavaScript that follows the language semantics without simplifications. JaVerT 2.0 supports whole-program symbolic testing, verification, and, for the first time, automatic compositional testing based on bi-abduction. The meta-theory underpinning JaVerT 2.0 is developed modularly, streamlining the proofs and informing the implementation. Our explicit treatment of symbolic execution errors allows us to give meaningful feedback to the developer during whole-program symbolic testing and guides the inference of resource of the bi-abductive execution. We evaluate the performance of JaVerT 2.0 on a number of JavaScript data-structure libraries, demonstrating: the scalability of our whole-program symbolic testing; an improvement over the state-of-the-art in JavaScript verification; and the feasibility of automatic compositional testing for JavaScript.

Symbolic analysis of switched-capacitor networks using compacted nodal analysis in the s-domain

This short paper presents a method that makes it possible to analyze switched-capacitor (SC) networks in discrete time using compacted nodal analysis in continuous time. Our objective is to perform time-discrete analysis in the z-domain using any symbolic analysis tool (e.g. CASCA) intended for analysis of time-continuous networks in the s-domain. A new equivalent analog circuit is used to describe the switched capacitors in the s-domain with z=s instead of z=e/sup sT/ which gives a simple translation between the two domains. Two SC examples are considered: an inverting amplifier and a biquad.

Efficient symbolic computation of approximated small-signal characteristics of analog integrated circuits

A symbolic analysis tool is presented that generates simplified symbolic expressions for the small-signal characteristics of large analog integrated circuits. The expressions are approximated while they are computed, so that only those terms are generated which remain in the final expression. This principle causes drastic savings in CPU time and memory, compared with previous symbolic analysis tools. In this way, the maximum size of circuits that can be analyzed, is largely increased. By taking into account a range for the value of a circuit parameter rather than one single number, the generated expressions are also more generally valid. Mismatch handling is explicitly taken into account in the algorithm. The capabilities of the new tool are illustrated with several experimental results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>