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ICSE 2020
Wed 24 June - Thu 16 July 2020
Thu 9 Jul 2020 01:05 - 01:17 at Baekje - P16-Security and Learning Chair(s): Lingming Zhang

Software defect prediction aims to automatically locate defective code modules to better focus testing resources and human effort. Typically, software defect prediction pipelines are comprised of two parts: the first extracts program features, like abstract syntax trees, by using external tools, and the second applies machine learning-based classification models to those features in order to predict defective modules. Since such approaches depend on specific feature extraction tools, machine learning classifiers have to be custom-tailored to effectively build most accurate models. To bridge the gap between deep learning and defect prediction, we propose an end-to-end framework which can directly get prediction results for programs without utilizing feature-extraction tools. To that end, we first visualize programs as images, apply the self-attention mechanism to extract image features, use transfer learning to reduce the difference in sample distribution between projects, and finally feed the image files into a pre-trained deep learning model for defect prediction. Experiments with 10 open source projects from the PROMISE dataset show that our method can improve cross-project and within-project defect prediction. Our code and data pointers are available at https://zenodo.org/record/3373409#.XV0Oy5Mza35.

Thu 9 Jul

Displayed time zone: (UTC) Coordinated Universal Time change

01:05 - 02:05
P16-Security and LearningTechnical Papers / Journal First at Baekje
Chair(s): Lingming Zhang The University of Texas at Dallas
01:05
12m
Talk
Software Visualization and Deep Transfer Learning for Effective Software Defect PredictionTechnical
Technical Papers
Jinyin Chen College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China, Keke Hu College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China, Yue Yu College of Computer, National University of Defense Technology, Changsha 410073, China, Zhuangzhi Chen College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China, Qi Xuan Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China, Yi Liu Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China, Vladimir Filkov University of California at Davis, USA
01:17
8m
Talk
Easy-to-Deploy API Extraction by Multi-Level Feature Embedding and Transfer LearningJ1
Journal First
Suyu Ma Monash University, Zhenchang Xing Australia National University, Chunyang Chen Monash University, Cheng Chen PricewaterhouseCoopers Firm, Lizhen Qu Monash University, Guoqiang Li Shanghai Jiao Tong University
01:25
12m
Talk
How Does Misconfiguration of Analytic Services Compromise Mobile Privacy?TechnicalArtifact Available
Technical Papers
Xueling Zhang University of Texas at San Antonio, Xiaoyin Wang University of Texas at San Antonio, USA, Rocky Slavin University of Texas at San Antonio, Travis Breaux Carnegie Mellon University, Jianwei Niu University of Texas at San Antonio
01:37
12m
Talk
Securing UnSafe Rust Programs with XRustArtifact ReusableTechnical
Technical Papers
Peiming Liu Texas A&M University, Gang Zhao Texas A&m University, Jeff Huang Texas A&M University
01:49
12m
Talk
Is Rust Used Safely by Software Developers?Technical
Technical Papers
Ana Nora Evans University of Virginia, USA, Bradford Campbell University of Virginia, Mary Lou Soffa University of Virginia