Superfit Combinational Elusive Bug Detection

Barzin, Reza; Fukushima, Shintaro; Howden, William E.; Sharifi, Shervin

doi:10.1109/compsac.2008.5

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…We used similar test fixtures to map from the test model to the steps that should appear in a FIT test table. A more detailed description of this use of the FIT testing strategy is contained in (Barzin, 2008).…”

Section: Automated Test Data Generation and Validation Oraclementioning

confidence: 99%

Elusive Bugs, Bounded Exhaustive Testing and Incomplete Oracles

Howden¹

2008

Proceedings of the Third International Conference on Software and Data Technologies Special Session on Applications in Banking

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Elusive bugs involve combinations of conditions that may not fit into any informal or intuitive testing scheme. One way to attack them is with Bounded Exhaustive Testing, in which all combinations of inputs for a bounded version of an application are tested. Studies of BET effectiveness for known bugs indicate that it is a promising approach. Because of the numbers of tests that are involved, BET normally depends on automated test generation and execution. This in turn requires the use of an automated oracle. In some cases the construction of a complete automated oracle would require the development of a second version of the application. This may be avoidable if incomplete oracles are used. Two classes of incomplete oracles are identified: necessity and sufficiency oracles. Examples are given of experiments using a necessity and a sufficiency oracle.

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Section: Automated Test Data Generation and Validation Oraclementioning

confidence: 99%

Elusive Bugs, Bounded Exhaustive Testing and Incomplete Oracles

Howden¹

2008

Proceedings of the Third International Conference on Software and Data Technologies Special Session on Applications in Banking

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“…The properties are either discrete or continuous [9] . The classification property is l, which is a discrete variable with range of L. The target of classification is minimize the misclassification error , that is for each :…”

Section: Behavior Distancementioning

confidence: 99%

Software Defect Regions Identification Based on Behavior Mining

Guo¹,

Wang²,

Wang³

2015

Advances in Computer Science Research

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Abstract. Random testing is a widely used testing technique, and adaptive random testing has enhanced the performance of random testing recently. The research results demonstrate that the improvement depends on the characters of the software defect region. A method that depicts the distribution of software defect region in the input region based on testing is proposed in this paper, and the method that describes the characteristics of the software defect region. The experimental results show that testing constraint reveals the mechanism of software defect trigging and propagation.

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“…j 在ϕ所占的比例.其中,||ϕ j ||表示失效域ϕ j 的约束值域,该比例越大,则说明属于失效域ϕ j 中能够检测出故障α的测试用例数越多. 基于测试约束还可以进行软件失效域的其他特征分析,如约束强度分析、包含关系分析等.其中,软件失效域的约束强度分析可以用来解释软件测试过程中为何一些难以捉摸的故障检测起来非常困难这一情况[26] .4 案例研究失效域的特征.TCAS 是欧洲宇航局飞机导航系统中的一个程序,完成避免飞机碰撞的主要功能.TCAS 为 C 程序,包含 9 个模块和 138 行可执行代码(http://pleuma.cc.gatech.edu/aristotle/Tools/subjects/tcas.tar.gz).TCAS 有 12 个输入参数,其中 5 个为布尔型,7 个为整型.选择 TCAS 作为案例研究的对象,是因为该程序被广泛用于软件测试方面的经验研究且具有丰富的测试资源 [27,28] .在 TCAS 的原始版本中,人工植入各种类型故障得到 41 个错误版本(即变异体).每个错误版本是通过对原始版本中的一行或多行代码实施变异算子获得的.来自西门子有经验的软件工程师设计了这些故障,因此可以认为这些类型的故障是工程实践中常见的. 作为示例,我们随机选取第 6 个故障,简要讨论其测试约束的构造过程.与 TCAS 原始版本相比,包含该故障 α的错误版本 f 在函数 Own_Below_Threat(⋅)中(源程序的第 104 行处)存在一个操作符变异,即将关系符"<"错误地写成"<=".故障α涉及到输入参数 Other_Tracked_Alt 和 Own_Tracked_Alt,进行故障敏感的偏移量约束条件分析得到 Offset(α)为 Other_Tracked_Alt=Own_Tracked_Alt (#1) 为了获取α的故障触发前提条件 Pre-Condition(α),必须分析故障α所在的函数 Own_Below_Threat(⋅)何时被调用.通过分析 TCAS 的程序依赖图、函数顺序调用图以及各个函数的控制流程图,不难发现如下函数调用链:main(⋅)→alt_sep_test(⋅)→Own_Below_Threat(⋅),main(⋅)→alt_sep_test(⋅)→Non_Crossing-Biased_Climb(⋅)→…”

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A Constraint-Based Approach to Identifying and Analyzing Failure-Causing Regions

Sun¹

2012

Journal of Software

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Random testing is a widely practiced black-box testing technique. Recently, adaptive random testing has been proposed to improve the random testing, and simulation results show that the improvements depend on the characteristic of failure-causing regions of program under test. This paper presents the concept of test constraints and employs them to specify the distribution of failure-causing regions within the input domain of program under test. Characteristic analysis of failure-causing regions can be conducted on the base of their test constraints, which are derived using the available program analysis techniques. To evaluate the proposed technique, a case study on a real-life application was conducted, and the results show that the proposed test constraint provides an insight into how a failure is triggered and propagated, and the constraint-based analysis helps to improve the quality of test case design and assess the applicability of the adaptive random testing.

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Superfit Combinational Elusive Bug Detection

Abstract: Software

Cited by 3 publications

References 25 publications

Elusive Bugs, Bounded Exhaustive Testing and Incomplete Oracles

Elusive Bugs, Bounded Exhaustive Testing and Incomplete Oracles

Software Defect Regions Identification Based on Behavior Mining

A Constraint-Based Approach to Identifying and Analyzing Failure-Causing Regions

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