XSTRESSOR : Automatic Generation of Large-Scale Worst-Case Test Inputs by Inferring Path Conditions

Saumya, Charitha; Koo, Jinkyu; Kulkarni, Milind; Bagchi, Saurabh

doi:10.1109/icst.2019.00011

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…In this case, Algorithm 2 can be used to generate positive tests for the new version using the old generator model. Otherwise, the discriminator model is trained with the newly executed tests and it will contest with the generator model until the FJD distance between the synthetic tests and the executed ones becomes low again (lines [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The contest procedure is the same as that of Algorithm 1.…”

Section: Acta Applied To Devopsmentioning

confidence: 99%

“…However, finding those values is mostly manual, intellectually intensive and laborious [30]. In recent years, researchers have proposed search-based profilers [21], [30], fuzzers [17], symbolic execution methods [6], [7], [26], and machine learning (ML) methods [14], [20], [25] to find those input values automatically and in a cost-effective way. However, most of these methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of these methods (e.g. [7], [21], [26], [30]) are suitable for whitebox settings, where we have access to the source code of the SUT, which is not true in most cases [1]. Methods based on symbolic execution do not scale to systems with a large set of input data, since the number of paths to search grows exponentially with respect to the input size [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Performance Testing Based on Active Deep Learning

Sedaghatbaf¹,

Moghadam²,

Saadatmand³

2021

Preprint

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Generating tests that can reveal performance issues in large and complex software systems within a reasonable amount of time is a challenging task. On one hand, there are numerous combinations of input data values to explore. On the other hand, we have a limited test budget to execute tests. What makes this task even more difficult is the lack of access to source code and the internal details of these systems. In this paper, we present an automated test generation method called ACTA for black-box performance testing. ACTA is based on active learning, which means that it does not require a large set of historical test data to learn about the performance characteristics of the system under test. Instead, it dynamically chooses the tests to execute using uncertainty sampling. ACTA relies on a conditional variant of generative adversarial networks, and facilitates specifying performance requirements in terms of conditions and generating tests that address those conditions. We have evaluated ACTA on a benchmark web application, and the experimental results indicate that this method is comparable with random testing, and two other machine learning methods, i.e. PerfXRL and DN.

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Section: Acta Applied To Devopsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Performance Testing Based on Active Deep Learning

Sedaghatbaf¹,

Moghadam²,

Saadatmand³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In [6], a reinforcement learning based approach was proposed for identifying how individual UI widgets are interacting. Saumya et al introduced the idea of the automatic generation of worst case test inputs from a model of program behavior in order to test programs under extreme loads [7]. More detailed descriptions on approaches for automated testing of mobile apps were introduced in [8].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Detection of Changes in Usage-Phases of a Mobile App

2020

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Under the fierce competition and budget constraints, most mobile apps are launched without sufficient tests. Thus, there exists a great demand for automated app testing. Recent developments in various machine learning techniques have made automated app testing a promising alternative to manual testing. This work proposes novel approaches for one of the core functionalities of automated app testing: the detection of changes in usage-phases of a mobile app. Because of the flexibility of app development languages and the lack of standards, each mobile app is very different from other apps. Furthermore, the graphical user interfaces for similar functionalities are rarely consistent or similar. Thus, we propose methods detecting usage-phase changes through object recognition and metrics utilizing graphs and generative models. Contrary to the existing change detection methods requiring learning models, the proposed methods eliminate the burden of training models. This elimination of training is suitable for mobile app testing whose typical usage-phase is composed of less than 10 screenshots. Our experimental results on commercial mobile apps show promising improvement over the state-of-the-practice method based on SIFT (scale-invariant feature transform).

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“…Classically, code coverage has been used as an analytical approach with the test suite execution. The approach is also used with the test suite generation strategies to maximizing the code-base covered by generating more test cases [8]. We have recently used more advanced techniques function as gray-box methods for test case analysis and test generation [2].…”

Section: Introductionmentioning

confidence: 99%

Code Coverage Aware Test Generation Using Constraint Solver

Sykora¹,

Ahmed²,

Bureš³

2020

Preprint

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Code coverage has been used in the software testing context mostly as a metric to assess a generated test suite's quality. Recently, code coverage analysis is used as a white-box testing technique for test optimization. Most of the research activities focus on using code coverage for test prioritization and selection within automated testing strategies. Less effort has been paid in the literature to use code coverage for test generation. This paper introduces a new Code Coverage-based Test Case Generation (CCTG) concept that changes the current practices by utilizing the code coverage analysis in the test generation process. CCTG uses the code coverage data to calculate the input parameters' impact for a constraint solver to automate the generation of effective test suites. We applied this approach to a few real-world case studies. The results showed that the new test generation approach could generate effective test cases and detect new faults.

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XSTRESSOR : Automatic Generation of Large-Scale Worst-Case Test Inputs by Inferring Path Conditions

Cited by 9 publications

References 25 publications

Automated Performance Testing Based on Active Deep Learning

Automated Performance Testing Based on Active Deep Learning

Unsupervised Detection of Changes in Usage-Phases of a Mobile App

Code Coverage Aware Test Generation Using Constraint Solver

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