Thu 12 May 2022 21:10 - 21:15 at ICSE room 2-odd hours - Machine Learning with and for SE 8 Chair(s): Seok-Won Lee
Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers rely on static analysis tools to regularly scan their code-bases and find potential bugs. In order to maximize coverage, however, these tools generally tend to report a significant amount of false positives, requiring developers to manually verify each warning. To address this problem, we propose a Transformer-based learning approach to identify false positive bug warnings. We demonstrate that our models can improve the precision of static analysis by 17.5%. In addition, we validated the generalizability of this approach across two major bug types: null dereference and resource leak.
Wed 11 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:00 | Machine Learning with and for SE 10Technical Track / SEIP - Software Engineering in Practice / Journal-First Papers at ICSE room 1-odd hours Chair(s): Preetha Chatterjee Drexel University, USA | ||
11:00 5mTalk | Defect Reduction Planning (using TimeLIME) Journal-First Papers Authorizer link Pre-print Media Attached | ||
11:05 5mTalk | Automatic Fault Detection for Deep Learning Programs Using Graph Transformations Journal-First Papers Amin Nikanjam École Polytechnique de Montréal, Houssem Ben Braiek École Polytechnique de Montréal, Mohammad Mehdi Morovati École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal Link to publication DOI Media Attached | ||
11:10 5mTalk | Counterfactual Explanations for Models of Code SEIP - Software Engineering in Practice Jürgen Cito TU Wien and Meta, Işıl Dillig University of Texas at Austin, Vijayaraghavan Murali Meta Platforms, Inc., Satish Chandra Facebook Pre-print Media Attached | ||
11:15 5mTalk | VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning Technical Track Qibin Chen Carnegie Mellon University, Jeremy Lacomis Carnegie Mellon University, Edward J. Schwartz Carnegie Mellon University Software Engineering Institute, Graham Neubig Carnegie Mellon University, Bogdan Vasilescu Carnegie Mellon University, USA, Claire Le Goues Carnegie Mellon University DOI Pre-print Media Attached | ||
11:20 5mTalk | Towards Training Reproducible Deep Learning Models Technical Track Boyuan Chen Centre for Software Excellence, Huawei Canada, Mingzhi Wen Huawei Technologies, Yong Shi Huawei Technologies, Dayi Lin Centre for Software Excellence, Huawei, Canada, Gopi Krishnan Rajbahadur Centre for Software Excellence, Huawei, Canada, Zhen Ming (Jack) Jiang York University Pre-print Media Attached | ||
11:25 5mTalk | Learning to Reduce False Positives in Analytic Bug Detectors Technical Track Anant Kharkar Microsoft, Roshanak Zilouchian Moghaddam Microsoft, Matthew Jin Microsoft Corporation, Xiaoyu Liu Microsoft Corporation, Xin Shi Microsoft Corporation, Colin Clement Microsoft, Neel Sundaresan Microsoft Corporation Pre-print Media Attached | ||