arXiv subjects

Zhen Ming

Publications and source records attributed to Zhen Ming.

24 records · Page 2Linked to original sources

arXiv2022

Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets

Publicly available datasets are one of the key drivers for commercial AI software. The use of publicly available datasets is governed by dataset licenses. These dataset licenses outline the rights one is entitled to on a given dataset and the obligations that one must fulfil to enjoy such rights without any license compliance violations. Unlike standardized Open Source Software (OSS) licenses, existing dataset licenses are defined in an ad-hoc manner and do not clearly outline the rights and obligations associated with their usage. Further, a public dataset may be hosted in multiple locations and created from multiple data sources each of which may have different licenses. Hence, existing approaches on checking OSS license compliance cannot be used. In this paper, we propose a new approach to assessing the potential license compliance violations if a given publicly available dataset were to be used for building commercial AI software. We conduct a case study with our approach on 6 commonly used publicly available image datasets. Our results show that there exists potential risks of license violations associated with all of the studied datasets if they were used for commercial purposes.

cs.LG↗

arXiv2022

Towards Build Verifiability for Java-based Systems

Build verifiability refers to the property that the build of a software system can be verified by independent third parties and it is crucial for the trustworthiness of a software system. Various efforts towards build verifiability have been made to C/C++-based systems, yet the techniques for Java-based systems are not systematic and are often specific to a particular build tool (e.g., Maven). In this study, we present a systematic approach towards build verifiability on Java-based systems. Our approach consists of three parts: a unified build process, a tool that dynamically controls non-determinism during the build process, and another tool that eliminates non-equivalences by post-processing the build artifacts. We apply our approach on 46 unverified open source projects from Reproducible Central and 13 open source projects that are widely used by Huawei commercial products. As a result, 91% of the unverified Reproducible Central projects and 100% of the commercially adopted OSS projects are successfully verified with our approach. In addition, based on our experience in analyzing thousands of builds for both commercial and open source Java-based systems, we present 14 patterns that introduce non-equivalences in generated build artifacts and their respective mitigation strategies. Among these patterns, 11 (78%) are unique for Java-based system, whereas the remaining 3 (22%) are common with C/C++-based systems. The approach and the findings of this paper are useful for both practitioners and researchers who are interested in build verifiability.

cs.SE↗

arXiv2022

Towards a consistent interpretation of AIOps models

Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures. To avoid misleading practitioners, AIOps model interpretations should be consistent (i.e., different AIOps models on the same task agree with one another on feature importance). However, many AIOps studies violate established practices in the machine learning community when deriving interpretations, such as interpreting models with suboptimal performance, though the impact of such violations on the interpretation consistency has not been studied. In this paper, we investigate the consistency of AIOps model interpretation along three dimensions: internal consistency, external consistency, and time consistency. We conduct a case study on two AIOps tasks: predicting Google cluster job failures, and Backblaze hard drive failures. We find that the randomness from learners, hyperparameter tuning, and data sampling should be controlled to generate consistent interpretations. AIOps models with AUCs greater than 0.75 yield more consistent interpretation compared to low-performing models. Finally, AIOps models that are constructed with the Sliding Window or Full History approaches have the most consistent interpretation with the trends presented in the entire datasets. Our study provides valuable guidelines for practitioners to derive consistent AIOps model interpretation.

cs.LG↗

arXiv2022

Towards Training Reproducible Deep Learning Models

Reproducibility is an increasing concern in Artificial Intelligence (AI), particularly in the area of Deep Learning (DL). Being able to reproduce DL models is crucial for AI-based systems, as it is closely tied to various tasks like training, testing, debugging, and auditing. However, DL models are challenging to be reproduced due to issues like randomness in the software (e.g., DL algorithms) and non-determinism in the hardware (e.g., GPU). There are various practices to mitigate some of the aforementioned issues. However, many of them are either too intrusive or can only work for a specific usage context. In this paper, we propose a systematic approach to training reproducible DL models. Our approach includes three main parts: (1) a set of general criteria to thoroughly evaluate the reproducibility of DL models for two different domains, (2) a unified framework which leverages a record-and-replay technique to mitigate software-related randomness and a profile-and-patch technique to control hardware-related non-determinism, and (3) a reproducibility guideline which explains the rationales and the mitigation strategies on conducting a reproducible training process for DL models. Case study results show our approach can successfully reproduce six open source and one commercial DL models.

cs.LG↗

arXiv2020

Empirical Study on the Software Engineering Practices in Open Source ML Package Repositories

Recent advances in Artificial Intelligence (AI), especially in Machine Learning (ML), have introduced various practical applications (e.g., virtual personal assistants and autonomous cars) that enhance the experience of everyday users. However, modern ML technologies like Deep Learning require considerable technical expertise and resources to develop, train and deploy such models, making effective reuse of the ML models a necessity. Such discovery and reuse by practitioners and researchers are being addressed by public ML package repositories, which bundle up pre-trained models into packages for publication. Since such repositories are a recent phenomenon, there is no empirical data on their current state and challenges. Hence, this paper conducts an exploratory study that analyzes the structure and contents of two popular ML package repositories, TFHub and PyTorch Hub, comparing their information elements (features and policies), package organization, package manager functionalities and usage contexts against popular software package repositories (npm, PyPI, and CRAN). Through these studies, we have identified unique SE practices and challenges for sharing ML packages. These findings and implications would be useful for data scientists, researchers and software developers who intend to use these shared ML packages.

cs.SE↗

arXiv2020

An Exploratory Study on Machine Learning Model Stores

Recent advances in Artificial Intelligence, especially in Machine Learning (ML), have brought applications previously considered as science fiction (e.g., virtual personal assistants and autonomous cars) into the reach of millions of everyday users. Since modern ML technologies like deep learning require considerable technical expertise and resource to build custom models, reusing existing models trained by experts has become essential. This is why in the past year model stores have been introduced, which, similar to mobile app stores, offer organizations and developers access to pre-trained models and/or their code to train, evaluate, and predict samples. This paper conducts an exploratory study on three popular model stores (AWS marketplace, Wolfram neural net repository, and ModelDepot) that compares the information elements (features and policies) provided by model stores to those used by the two popular mobile app stores (Google Play and Apple's App Store). We have found that the model information elements vary among the different model stores, with 65% elements shared by all three studied stores. Model stores share five information elements with mobile app stores, while eight elements are unique to model stores and four elements unique to app stores. Only few models were available on multiple model stores. Our findings allow to better understand the differences between ML models and "regular" source code components or applications, and provide inspiration to identify software engineering practices (e.g., in requirements and delivery) specific to ML applications.

cs.SE↗