Jiho Shin — Page 2 | Search arXiv

arXiv2022

Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries

The application of machine learning (ML) libraries has been tremendously increased in many domains, including autonomous driving systems, medical, and critical industries. Vulnerabilities of such libraries result in irreparable consequences. However, the characteristics of software security vulnerabilities have not been well studied. In this paper, to bridge this gap, we take the first step towards characterizing and understanding the security vulnerabilities of five well-known ML libraries, including Tensorflow, PyTorch, Sickit-learn, Pandas, and Numpy. To do so, in total, we collected 596 security-related commits to exploring five major factors: 1) vulnerability types, 2) root causes, 3) symptoms, 4) fixing patterns, and 5) fixing efforts of security vulnerabilities in ML libraries. The findings of this study can assist developers in having a better understanding of software security vulnerabilities across different ML libraries and gain a better insight into their weaknesses of them. To make our finding actionable, we further developed DeepMut, an automated mutation testing tool, as a proof-of-concept application of our findings. DeepMut is designed to assess the adequacy of existing test suites of ML libraries against security-aware mutation operators extracted from the vulnerabilities studied in this work. We applied DeepMut on the Tensorflow kernel module and found more than 1k alive mutants not considered by the existing test suits. The results demonstrate the usefulness of our findings.

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arXiv2021

Explainable Software Defect Prediction: Are We There Yet?

Explaining the prediction results of software defect prediction models is a challenging while practical task, which can provide useful information for developers to understand and fix the predicted bugs. To address this issue, recently, Jiarpakdee et al. proposed to use {two state-of-the-art} model-agnostic techniques (i.e., LIME and BreakDown) to explain the prediction results of bug prediction models. Their experiments show these tools can generate promising results and the generated explanations can assist developers understand the prediction results. However, the fact that LIME and BreakDown were only examined on a single software defect prediction model setting calls into question about their consistency and reliability across software defect prediction models with various settings. In this paper, we set out to investigate the consistency and reliability of model-agnostic technique based explanation generation approaches (i.e., LIME and BreakDown) on software defect prediction models with different settings , e.g., different data sampling techniques, different machine learning classifiers, and different prediction scenarios. Specifically, we use both LIME and BreakDown to generate explanations for the same instance under software defect prediction models with different settings and then check the consistency of the generated explanations for the instance. We reused the same defect data from Jiarpakdee et al. in our experiments. The results show that both LIME and BreakDown generate inconsistent explanations under different software defect prediction settings for the same test instances, which makes them unreliable for explanation generation. Overall, with this study, we call for more research in explainable software defect prediction towards achieving consistent and reliable explanation generation.

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