Testing machine learning based systems: a systematic mapping

Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towa...

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Veröffentlicht in:Empirical software engineering : an international journal Jg. 25; H. 6; S. 5193 - 5254
Hauptverfasser: Riccio, Vincenzo, Jahangirova, Gunel, Stocco, Andrea, Humbatova, Nargiz, Weiss, Michael, Tonella, Paolo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2020
Springer Nature B.V
Schlagworte:
Adequacy
Algorithms
Compilers
Computer Science
Context
Evaluation
Functional testing
Interpreters
Machine learning
Mapping
Programming Languages
Quality assurance
Safety critical
Software
Software Engineering/Programming and Operating Systems
Software testing
Software testing
Systematic review
Systematic mapping
Machine learning
ISSN:1382-3256, 1573-7616
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Zusammenfassung:Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towards the quality assurance of such systems. Despite the advances in software testing, MLSs bring novel and unprecedented challenges, since their behaviour is defined jointly by the code that implements them and the data used for training them. Objective: To identify the existing solutions for functional testing of MLSs, and classify them from three different perspectives: (1) the context of the problem they address, (2) their features, and (3) their empirical evaluation. To report demographic information about the ongoing research. To identify open challenges for future research. Method: We conducted a systematic mapping study about testing techniques for MLSs driven by 33 research questions. We followed existing guidelines when defining our research protocol so as to increase the repeatability and reliability of our results. Results: We identified 70 relevant primary studies, mostly published in the last years. We identified 11 problems addressed in the literature. We investigated multiple aspects of the testing approaches, such as the used/proposed adequacy criteria, the algorithms for test input generation, and the test oracles. Conclusions: The most active research areas in MLS testing address automated scenario/input generation and test oracle creation. MLS testing is a rapidly growing and developing research area, with many open challenges, such as the generation of realistic inputs and the definition of reliable evaluation metrics and benchmarks.
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ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-020-09881-0