Constrained Adversarial Learning for Automated Software Testing: a literature review
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866918024492089344 |
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| author | Vitorino, João Dias, Tiago Fonseca, Tiago Maia, Eva Praça, Isabel |
| author_facet | Vitorino, João Dias, Tiago Fonseca, Tiago Maia, Eva Praça, Isabel |
| contents | It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing tools can be developed to quickly analyze many lines of code and detect vulnerabilities by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial machine learning methods, so there could be significant benefits to the integration of these methods in testing tools to identify possible attack vectors. Therefore, this literature review is focused on the current state-of-the-art of constrained data generation approaches applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance their software testing tools with adversarial testing methods and improve the resilience and robustness of their information systems. The found approaches were systematized, and the advantages and limitations of those specific for white-box, grey-box, and black-box testing were analyzed, identifying research gaps and opportunities to automate the testing tools with data generated by adversarial attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_07546 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Constrained Adversarial Learning for Automated Software Testing: a literature review Vitorino, João Dias, Tiago Fonseca, Tiago Maia, Eva Praça, Isabel Software Engineering Machine Learning It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing tools can be developed to quickly analyze many lines of code and detect vulnerabilities by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial machine learning methods, so there could be significant benefits to the integration of these methods in testing tools to identify possible attack vectors. Therefore, this literature review is focused on the current state-of-the-art of constrained data generation approaches applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance their software testing tools with adversarial testing methods and improve the resilience and robustness of their information systems. The found approaches were systematized, and the advantages and limitations of those specific for white-box, grey-box, and black-box testing were analyzed, identifying research gaps and opportunities to automate the testing tools with data generated by adversarial attacks. |
| title | Constrained Adversarial Learning for Automated Software Testing: a literature review |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2303.07546 |