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1. Towards Unified Data and Lifecycle Management for Deep Learning

   https://doi.org/10.1109/ICDE.2017.112  

   Miao, Hui; Li, Ang; Davis, Larry S.; Deshpande, Amol (April 2017, Data Engineering (ICDE), 2017 IEEE 33rd International Conference on)

   Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, e.g., learned parameters and training logs, and it comprises of several frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and largely left to the users. This paper describes our vision and implementation of a data and lifecycle management system for deep learning. First, we generalize model exploration and model enumeration queries from commonly conducted tasks by deep learning modelers, and propose a high-level domain specific language (DSL), inspired by SQL, to raise the abstraction level and thereby accelerate the modeling process. To manage the variety of data artifacts, especially the large amount of checkpointed float parameters, we design a novel model versioning system (dlv), and a read-optimized parameter archival storage system (PAS) that minimizes storage footprint and accelerates query workloads with minimal loss of accuracy. PAS archives versioned models using deltas in a multi-resolution fashion by separately storing the less significant bits, and features a novel progressive query (inference) evaluation algorithm. Third, we develop efficient algorithms for archiving versioned models using deltas under co-retrieval constraints. We conduct extensive experiments over several real datasets from computer vision domain to show the efficiency of the proposed techniques. 

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2. Benchmark of DNN Model Search at Deployment Time

   https://doi.org/10.1145/3538712.3538725  

   Zhou, Lixi; Jain, Arindam; Wang, Zijie; Das, Amitabh; Yang, Yingzhen; Zou, Jia (July 2022, SSDBM '22: Proceedings of the 34th International Conference on Scientific and Statistical Database Management)

   Deep learning has become the most popular direction in machine learning and artificial intelligence. However, the preparation of training data, as well as model training, are often time-consuming and become the bottleneck of the end-to-end machine learning lifecycle. Reusing models for inferring a dataset can avoid the costs of retraining. However, when there are multiple candidate models, it is challenging to discover the right model for reuse. Although there exist a number of model-sharing platforms such as ModelDB, TensorFlow Hub, PyTorch Hub, and DLHub, most of these systems require model uploaders to manually specify the details of each model and model downloaders to screen keyword search results for selecting a model. We are lacking a highly productive model search tool that selects models for deployment without the need for any manual inspection and/or labeled data from the target domain. This paper proposes multiple model search strategies including various similarity-based approaches and non-similarity-based approaches. We design, implement and evaluate these approaches on multiple model inference scenarios, including activity recognition, image recognition, text classification, natural language processing, and entity matching. The experimental evaluation showed that our proposed asymmetric similarity-based measurement, adaptivity, outperformed symmetric similarity-based measurements and non-similarity-based measurements in most of the workloads. 

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3. Toward Predicting Architectural Significance of Implementation Issues

   Shahbazian, Arman; Nam, Daye; Medvidovic, Nenad (May 2018, International Conference on Mining Software Repositories)

   In a software system’s development lifecycle, engineers make numerous design decisions that subsequently cause architectural change in the system. Previous studies have shown that, more often than not, these architectural changes are unintentional by-products of continual software maintenance tasks. The result of inadvertent architectural changes is accumulation of technical debt and deterioration of software quality. Despite their important implications, there is a relative shortage of techniques, tools, and empirical studies pertaining to architectural design decisions. In this paper, we take a step toward addressing that scarcity by using the information in the issue and code repositories of open-source software systems to investigate the cause and frequency of such architectural design decisions. Furthermore, building on these results, we develop a predictive model that is able to identify the architectural significance of newly submitted issues, thereby helping engineers to prevent the adverse effects of architectural decay. The results of this study are based on the analysis of 21,062 issues affecting 301 versions of 5 large open-source systems for which the code changes and issues were publicly accessible. 

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4. Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning

   https://doi.org/10.1109/ICSE55347.2025.00220  

   Manke, Ruchira; Wardat, Mohammad; Khomh, Foutse; Rajan, Hridesh (April 2025, IEEE)

   While deep learning (DL) has permeated, and become an integral component of many critical software systems, today software engineering research hasn't explored how to separately test data and models that are integral for DL approaches to work effectively. The main challenge in independently testing these components arises from the tight dependency between data and models. This research explores this gap, introducing our methodology of mock deep testing for unit testing of DL applications. To enable unit testing, we introduce a design paradigm that decomposes the workflow into distinct, manageable components, minimizes sequential dependencies, and modularizes key stages of the DL, including data preparation and model design. For unit testing these components, we propose modeling their dependencies using mocks. In the context of DL, mocks refer to mock data and mock model that mimic the behavior of the original data and model, respectively. This modular approach facilitates independent development and testing of the components, ensuring comprehensive quality assurance throughout the development process. We have developed KUnit, a framework for enabling mock deep testing for the Keras library, a popular library for developing DL applications. We empirically evaluated KUnit to determine the effectiveness of mocks in independently testing data and models. Our assessment of 50 DL programs obtained from Stack Overflow and GitHub shows that mocks effectively identified 10 issues in the data preparation stage and 53 issues in the model design stage. We also conducted a user study with 36 participants using KUnit to perceive the effectiveness of our approach. Participants using KUnit successfully resolved 25 issues in the data preparation stage and 38 issues in the model design stage. Our findings highlight that mock objects provide a lightweight emulation of the dependencies for unit testing, facilitating early bug detection. Lastly, to evaluate the usability of KUnit, we conducted a post-study survey. The results reveal that KUnit is helpful to DL application developers, enabling them to independently test each component (data and model) and resolve issues effectively in different stages. 

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5. Attacking and Protecting Data Privacy in Edge-Cloud Collaborative Inference Systems

   https://doi.org/10.1109/JIOT.2020.3022358  

   He, Zecheng; Zhang, Tianwei; Lee, Ruby B. (September 2020, IEEE Internet of Things Journal)

   null (Ed.)

   Benefiting from the advance of Deep Learning technology, IoT devices and systems are becoming more intelligent and multi-functional. They are expected to run various Deep Learning inference tasks with high efficiency and performance. This requirement is challenged by the mismatch between the limited computing capability of edge devices and large-scale Deep Neural Networks. Edge-cloud collaborative systems are then introduced to mitigate this conflict, enabling resource-constrained IoT devices to host arbitrary Deep Learning applications. However, the introduction of third-party clouds can bring potential privacy issues to edge computing. In this paper, we conduct a systematic study about the opportunities of attacking and protecting the privacy of edge-cloud collaborative systems. Our contributions are twofold: (1) we first devise a set of new attacks for an untrusted cloud to recover arbitrary inputs fed into the system, even if the attacker has no access to the edge device’s data or computations, or permissions to query this system. (2) We empirically demonstrate that solutions that add noise fail to defeat our proposed attacks, and then propose two more effective defense methods. This provides insights and guidelines to develop more privacy-preserving collaborative systems and algorithms. 

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