Getting ready for BigData testing: A practitioner's perception

Nachiyappan, S.; Selwyn, Justus

doi:10.1109/icccnt.2013.6726822

Cited by 10 publications

(11 citation statements)

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“…Despite the testing challenges of the Big Data applications [15,16] and the progresses in the testing techniques [17], little effort is focused on testing the MapReduce programs [18], one of the principal paradigms of Big Data [19]. A study of Kavulya et al [20] analyses several MapReduce programs and 3% of them do not finish, while another study by Ren et al [21] places the number between 1.38% and 33.11%.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure-Aware Functional Testing of MapReduce Programs

Morán

Rivas

Riva

et al. 2016

2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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Abstract-Programs that process a large volume of data generally run in a distributed and parallel architecture, such as the programs implemented in the processing model MapReduce. In these programs, developers can abstract the infrastructure where the program will run and focus on the functional issues. However, the infrastructure configuration and its state cause different parallel executions of the program and some could derive in functional faults which are hard to reveal. In general, the infrastructure that executes the program is not considered during the testing, because the tests usually contain few input data and then the parallelization is not necessary. In this paper a testing technique is proposed to generate different infrastructure configurations for a given test input data, and then the program is executed in these configurations in order to reveal functional faults. This testing technique is automatized by using a test engine and applied in a case study. As a result, several infrastructure configurations are automatically generated and executed for a test case revealing a functional fault that is then fixed by the developer.

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Section: Related Workmentioning

confidence: 99%

Infrastructure-Aware Functional Testing of MapReduce Programs

Morán

Rivas

Riva

et al. 2016

2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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“…However, there is little published research in performing testing on applications that already interact with big data [9]. Moreover, even fewer publications explore how search-based software testing (SBST) techniques can be used to optimize testing strategies [6,8].…”

Section: Overviewmentioning

confidence: 99%

Extending search-based software testing techniques to big data applications

Fredericks

Hariri

2016

Proceedings of the 9th International Workshop on Search-Based Software Testing

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Massive datasets are quickly becoming a concern for many industries. For example, many web-based applications must be able to handle petabytes worth of transactions on a daily basis, and moreover, be able to quickly and efficiently act upon data that exists in each transaction. As a result, providing testing capabilities for such applications becomes a challenge of scale. We argue that existing approaches, such as automated test suite generation, may not necessarily scale without assistance. To this end, we discuss open issues and possible solutions specific to testing big data applications. CCS Concepts•Software and its engineering → Software testing and debugging; Search-based software engineering; Software system structures;Keywords big data, search-based software testing, test suite generation OVERVIEWMany techniques are currently being developed for generating datasets of massive scale (i.e., big data) for use in validating applications [1]. However, there is little published research in performing testing on applications that already interact with big data [9]. Moreover, even fewer publications explore how search-based software testing (SBST) techniques can be used to optimize testing strategies [6,8]. As such, research needs to be performed in testing big data applications to determine both the feasibility and applicability of existing testing techniques to such applications. For example, consider a nationwide healthcare network that centralizes medical records for all patients. Such a system can deals with an enormous amount of data as well as an amalgam of heterogeneous systems and devices. This system can enable a patient to visit their primary care physician, receive a prescription for treatment with a specialist in another state, and then enable that specialist to instantly retrieve the entirety of the patient's medical history. As such, specialized applications will require development to handle the dataset, including optimizations for querying and retrieving specific data. However, such applications may not be effectively tested by existing strategies, given the wide range of values that may manifest. As such, this position paper specifically argues for an examination on how big data can impact existing testing strategies, focusing on automated test suite generation.Traditionally, software testing has been considered an ideal field for application of search-based heuristics, such as genetic algorithms [7]. Notable systems include EvoSuite [5] and Nighthawk [2] for automated generation of test suites and instantiation of unit tests, respectively. Given the optimization problems that typically comprise a software testing strategy (e.g., test suite generation, test case prioritization and selection, etc.), search-based heuristics have been shown to quickly and efficiently come to an optimal solution. However, many industries are moving towards the big data paradigm, where petabytes of data must be considered at run time. As such, a strategy such as test suite generation may be cost-prohibitive, given t...

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“…This field has experimented great progress in recent years [24], but there are still some challenges to test Big Data programs [25], [26]. Despite the fact that most works are focused on performance testing [27], [28], functional testing is also important [29].…”

Section: Related Workmentioning

confidence: 99%

Towards Ex Vivo Testing of MapReduce Applications

Morán

Bertolino

Riva

et al. 2017

2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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Abstract-Big Data programs are those that process large data exceeding the capabilities of traditional technologies. Among newly proposed processing models, MapReduce stands out as it allows the analysis of schema-less data in large distributed environments with frequent infrastructure failures. Functional faults in MapReduce are hard to detect in a testing/preproduction environment due to its distributed characteristics. We propose an automatic test framework implementing a novel testing approach called Ex Vivo. The framework employs data from production but executes the tests in a laboratory to avoid side-effects on the application. Faults are detected automatically without human intervention by checking if the same data would generate different outputs with different infrastructure configurations. The framework (MrExist) is validated with a real-world program. MrExist can identify a fault in a few seconds, then the program can be stopped, not only avoiding an incorrect output, but also saving money, time and energy of production resources.

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Getting ready for BigData testing: A practitioner's perception

Cited by 10 publications

References 0 publications

Infrastructure-Aware Functional Testing of MapReduce Programs

Infrastructure-Aware Functional Testing of MapReduce Programs

Extending search-based software testing techniques to big data applications

Towards Ex Vivo Testing of MapReduce Applications

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