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Title:Aiding automated testing generation process by visualizing dynamic symbolic execution
Author(s):Cao, Jiayi
Advisor(s):Xie, Tao
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Visualization, Dynamic Symbolic Execution, Test Input Generation
Abstract:Significant progress has been made in the field of automatic test generation, and Dynamic Symbolic Execution (DSE) is among the most effective techniques in this field. Despite its success, DSE still suffers from various obstacles when applied on complex programs. Users of DSE-based testing tools often experience three main categories of problems: the object creation problem, external method call problem, and boundary problem. When these problems arise, the testing tools present little information about the problem causes, leaving the users in the dark. Furthermore, there is little visual (i.e., easy to digest) guidance readily available to solve the problems effectively. The lack of guidance is especially troublesome given that the tools do not scale well when the number of problems increases. As a result, the time needed to investigate the root causes of the problems is prohibitive. To address such issue, in this thesis, we propose a visualization approach named PexViz. Our approach helps the tool users better understand and diagnose the encountered problems by reducing the large search space for problem root causes by aggregating information gathered through DSE exploration. In this thesis, we illustrate the benefits of the visualized information in assisting the tool users and our design decisions for such visualization. We provide a comparison between the proposed approach and two related previous approaches: IntelliTest, an industrial test generator available in Visual Studio 2015/2017, and an existing state-of-the-art visualization approach, SEViz. Our initial comparison results on example cases demonstrate the benefits of our proposed approach and its superiority over the related approaches.
Issue Date:2018-04-27
Rights Information:Copyright 2018 Jiayi Cao
Date Available in IDEALS:2018-09-04
Date Deposited:2018-05

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