Write a Blog >>
ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Wed 23 Sep 2020 00:00 - 00:20 at Kangaroo - Software Engineering for AI (1) Chair(s): Song Wang

With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in demand to improve the quality of DL models. One of the major challenges is the data gap between the training data to construct the models and the testing data to evaluate them. To bridge the gap, testers aims to collect an effective subset of inputs from the testing contexts, with limited labeling effort, for retraining DL models.

To assist the subset selection, we propose \textbf{M}ultiple-Boundary \textbf{C}lustering and \textbf{P}rioritization (\textbf{MCP}), a technique to cluster test samples into the boundary areas of multiple boundaries for DL models and specify the priority to select samples evenly from all boundary areas, to make sure enough useful samples for each boundary reconstruction.

To evaluate MCP, we conduct an extensive empirical study with three popular DL models and 33 simulated testing contexts. The experiment results show that, compared with state-of-the-art baseline methods, on effectiveness, our approach MCP has a significantly better performance by evaluating the improved quality of retrained DL models; on efficiency, MCP also has the advantages in time costs.

Wed 23 Sep
Times are displayed in time zone: (UTC) Coordinated Universal Time

00:00 - 01:00: Software Engineering for AI (1)Research Papers / NIER track at Kangaroo
Chair(s): Song WangYork University, Canada
00:00 - 00:20
Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining
Research Papers
Weijun ShenNanjing University, Yanhui LiDepartment of Computer Science and Technology, Nanjing University, Lin ChenNanjing University, YuanLei HanNanjing University, Yuming ZhouNanjing University, Baowen XuState Key Laboratory for Novel Software Technology, Nanjing University
00:20 - 00:40
MARBLE: Model-Based Robustness Analysis of Stateful Deep Learning Systems
Research Papers
Xiaoning DuNanyang Technological University, Yi LiNanyang Technological University, Singapore, Xiaofei XieNanyang Technological University, Lei MaKyushu University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University
00:40 - 00:50
Making Fair ML Software using Trustworthy Explanation
NIER track
Joymallya ChakrabortyNorth Carolina State University, USA, Kewen PengNorth Carolina State University, Tim MenziesNorth Carolina State University, USA
Link to publication DOI Pre-print Media Attached