We present SymJS, a comprehensive framework for automatic testing of client-side JavaScript Web applications. The tool contains a symbolic execution engine for JavaScript, and an automatic event explorer for Web pages. Without any user intervention, SymJS can automatically discover and explore Web events, symbolically execute the associated JavaScript code, refine the execution based on dynamic feedbacks, and produce test cases with high coverage. The symbolic engine contains a symbolic virtual machine, a string-numeric solver, and a symbolic executable DOM model. SymJS's innovations include a novel symbolic virtual machine for JavaScript Web, symbolic+dynamic feedback directed event space exploration, and dynamic taint analysis for enhancing event sequence construction. We illustrate the effectiveness of SymJS on standard JavaScript benchmarks and various real-life Web applications. On average SymJS achieves over 90% line coverage for the benchmark programs, significantly outperforming existing methods.
Abstract. We present the first symbolic execution and automatic test generation tool for C++ programs. First we describe our effort in extending an existing symbolic execution tool for C programs to handle C++ programs. We then show how we made this tool generic, efficient and usable to handle real-life industrial applications. Novel features include extended symbolic virtual machine, library optimization for C and C++, object-level execution and reasoning, interfacing with specific type of efficient solvers, and semi-automatic unit and component testing. This tool is being used to assist the validation and testing of industrial software as well as publicly available programs written using the C++ language.
Abstract-In this paper we present JST, a tool that automatically generates a high coverage test suite for industrial strength Java applications. This tool uses a numeric-string hybrid symbolic execution engine at its core which is based on the Symbolic Java PathFinder platform. However, in order to make the tool applicable to industrial applications the existing generic platform had to be enhanced in numerous ways that we describe in this paper. The JST tool consists of newly supported essential Java library components and widely used data structures; novel solving techniques for string constraints, regular expressions, and their interactions with integer and floating point numbers; and key optimizations that make the tool more efficient. We present a methodology to seamlessly integrate the features mentioned above to make the tool scalable to industrial applications that are beyond the reach of the original platform in terms of both applicability and performance. We also present extensive experimental data to illustrate the effectiveness of our tool.
Abstract. The problem of solving string constraints together with numeric constraints has received increasing interest recently. Existing methods use either bit-vectors or automata (or their combination) to model strings, and reduce string constraints to bit-vector constraints or automaton operations, which are then solved in the respective domain. Unfortunately, they often fail to achieve a good balance between efficiency, accuracy, and comprehensiveness. In this paper we illustrate a new technique that uses parameterized arrays as the main data structure to model strings, and converts string constraints into quantified expressions that are solved through quantifier elimination. We present an efficient and sound quantifier elimination algorithm. In addition, we use an automaton model to handle regular expressions and reason about string values faster. Our method does not need to enumerate string lengths (as bit-vector based methods do), or concrete string values (as automaton based methods do). Hence, it can achieve much better accuracy and efficiency. In particular, it can identify unsatisfiable cases quickly. Our solver (named PASS) supports most of the popular string operations, including string comparisons, string-numeric conversions, and regular expressions. Experimental results demonstrate the advantages of our method.
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