Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning
This program is tentative and subject to change.
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. We also found 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.
This program is tentative and subject to change.
Fri 2 MayDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | |||
16:00 15mTalk | Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Research Track Sigma Jahan Dalhousie University, Mehil Shah Dalhousie University, Parvez Mahbub Dalhousie University, Masud Rahman Dalhousie University | ||
16:15 15mTalk | Lightweight Concolic Testing via Path-Condition Synthesis for Deep Learning Libraries Research Track Sehoon Kim , Yonghyeon Kim UNIST, Dahyeon Park UNIST, Yuseok Jeon UNIST, Jooyong Yi UNIST, Mijung Kim UNIST | ||
16:30 15mTalk | Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning Research Track Ruchira Manke Tulane University, USA, Mohammad Wardat Oakland University, USA, Foutse Khomh Polytechnique Montréal, Hridesh Rajan Tulane University | ||
16:45 15mTalk | RUG: Turbo LLM for Rust Unit Test Generation Research Track Xiang Cheng Georgia Institute of Technology, Fan Sang Georgia Institute of Technology, Yizhuo Zhai Georgia Institute of Technology, Xiaokuan Zhang George Mason University, Taesoo Kim Georgia Institute of Technology | ||
17:00 15mTalk | Test Input Validation for Vision-based DL Systems: An Active Learning Approach SE In Practice (SEIP) Delaram Ghobari University of Ottawa, Mohammad Hossein Amini University of Ottawa, Dai Quoc Tran SmartInsideAI Company Ltd. and Sungkyunkwan University, Seunghee Park SmartInsideAI Company Ltd. and Sungkyunkwan University, Shiva Nejati University of Ottawa, Mehrdad Sabetzadeh University of Ottawa Pre-print | ||