Random unit-test generation with MUT-aware sequence recommendation

Zheng, Wujie; Zhang, Qirun; Lyu, Michael R.; Xie, Tao

doi:10.1145/1858996.1859054

Cited by 23 publications

(19 citation statements)

References 13 publications

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“…Depending on the information used, test input design and generation techniques have been broadly classified as specification based, structure based, and RT based [1], [21]. While both specification-based and structure-based approaches require detailed information about the OOS (either the specification or program structure), RT-based approaches have no such requirements, and can generate a large number of test inputs at low cost [1], [5], [8]- [10]. Furthermore, RT is conceptually simple, can easily be scaled, is readily applicable to many kinds of software, and has been widely used in industry [7].…”

Section: A Oo Software Testingmentioning

confidence: 99%

“…If p and q are objects of the same class, their type distance, based on the typeDist metric, will be zero, which would obviously be insufficient for differentiating between the two objects. To address this, we define the secDist metric [see Formula (10)] to distinguish between two objects' NRefV attributes which are of the same data type. …”

Section: Objdist(p Q)mentioning

confidence: 99%

“…6) The OMISS metric can differentiate between objects more precisely than ARTOO, even only based on the type, field, and recursive distances. It is because OMISS considers both the "recursive distance" [see Formula (7) for RefV attributes] and the "type distance" [see Formula (9)] as "primary distances," but the "field distance" as "secondary distance" [see Formula (10)]. This means that the internal structure of a class from which an object is built is considered more significant than the actual values used to initialize that object.…”

Section: E Summary Of Omiss Metric Advantagesmentioning

confidence: 99%

“…Many research studies have examined OOS testing using RT [5], [8]- [10], which can be easily applied to test at many levels (including unit, integration, and system levels) [5], [9], [10]. However, it has been noted that RT's effectiveness and efficiency deteriorate as the OOS size and complexity increase.…”

mentioning

confidence: 99%

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A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

et al. 2017

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Abstract-Random testing (RT) has been identified as one of the most popular testing techniques, due to its simplicity and ease of automation. Adaptive random testing (ART) has been proposed as an enhancement to RT, improving its fault-detection effectiveness by evenly spreading random test inputs across the input domain. To achieve the even spreading, ART makes use of distance measurements between consecutive inputs. However, due to the nature of object-oriented software (OOS), its distance measurement can be particularly challenging: Each input may involve multiple classes, and interaction of objects through method invocations. Two previous studies have reported on how to test OOS at a single-class level using ART. In this study, we propose a new similarity metric to enable multiclass level testing using ART. When generating test inputs (for multiple classes, a series of objects, and a sequence of method invocations), we use the similarity metric to calculate the distance between two series of objects, and between two sequences of method invocations. We integrate this metric with ART and apply it to a set of open-source OO programs, with the empirical results showing that our approach outperforms other RT and ART approaches in OOS testing.

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Section: A Oo Software Testingmentioning

confidence: 99%

Section: Objdist(p Q)mentioning

confidence: 99%

Section: E Summary Of Omiss Metric Advantagesmentioning

confidence: 99%

mentioning

confidence: 99%

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A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

et al. 2017

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“…Zheng et al [31] propose to select methods that are likely to affect the method under test, where influence between two methods is estimated based on whether the methods access a common field. These approaches increase the coverage of methods under test, whereas our guidance technique intensifies the API usage of the tested program.…”

Section: B Test Generationmentioning

confidence: 99%

Leveraging test generation and specification mining for automated bug detection without false positives

Pradel¹

2012

2012 34th International Conference on Software Engineering (ICSE)

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Abstract-Mining specifications and using them for bug detection is a promising way to reveal bugs in programs. Existing approaches suffer from two problems. First, dynamic specification miners require input that drives a program to generate common usage patterns. Second, existing approaches report false positives, that is, spurious warnings that mislead developers and reduce the practicability of the approach. We present a novel technique for dynamically mining and checking specifications without relying on existing input to drive a program and without reporting false positives. Our technique leverages automatically generated tests in two ways: Passing tests drive the program during specification mining, and failing test executions are checked against the mined specifications. The output are warnings that show with concrete test cases how the program violates commonly accepted specifications. Our implementation reports no false positives and 54 true positives in ten well-tested Java programs.

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Baton: symphony of random testing and concolic testing through machine learning and taint analysis

Chen

Liu

Peng

et al. 2022

Sci. China Inf. Sci.

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Random unit-test generation with MUT-aware sequence recommendation

Cited by 23 publications

References 13 publications

A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

Leveraging test generation and specification mining for automated bug detection without false positives

Baton: symphony of random testing and concolic testing through machine learning and taint analysis

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