Data dependence analysis underlies various applications in software quality assurance, yet existing frameworks/tools for this analysis commonly suffer scalability challenges. We present PCA, a static interprocedural data dependence analyzer for real-world C programs. PCA performs interprocedural points-to and data-flow analyses with a lightweight design. Most of all, it features a partial call-path (PCA) analysis that consists of optimization options to further speed up data dependence computation. As an example application of it, PCA readily supports memory leak detection, for which it helps achieve close or better performance and precision relative to the same application based on a state-of-the-art value flow analysis. In particular, it found four more memory leaks in an industry-scale system which have been fixed by the developers. Through the data dependence it computes, PCA can enable other applications (e.g., impact analysis and taint analysis).
Fri 13 NovDisplayed time zone: (UTC) Coordinated Universal Time change
01:00 - 01:30 | |||
01:00 2mTalk | ARDiff: Scaling Program Equivalence Checking via Iterative Abstraction and Refinement of Common Code Research Papers Sahar Badihi University of British Columbia, Canada, Faridah Akinotcho University of British Columbia, Canada, Yi Li Nanyang Technological University, Singapore, Julia Rubin University of British Columbia, Canada DOI Pre-print | ||
01:03 1mTalk | Java Ranger: Statically Summarizing Regions for Efficient Symbolic Execution of Java Research Papers Vaibhav Sharma University of Minnesota, USA, Soha Hussein University of Minnesota, USA / Ain Shams University, Egypt, Michael Whalen University of Minnesota, USA, Stephen McCamant University of Minnesota, USA, Willem Visser Stellenbosch University, South Africa DOI | ||
01:05 1mTalk | PCA: Memory Leak Detection using Partial Call-Path Analysis Tool Demos Wen Li , Haipeng Cai Washington State University, USA, Yulei Sui University of Technology Sydney, David Manz Pacific Northwest National Laboratory, USA DOI | ||
01:07 1mTalk | SWAN: A Static Analysis Framework for Swift Tool Demos Daniil Tiganov University of Alberta, Canada, Jeff Cho University of Alberta, Karim Ali University of Alberta, Julian Dolby IBM Research, USA DOI | ||
01:09 1mTalk | UBITect: A Precise and Scalable Method to Detect Use-before-Initialization Bugs in Linux Kernel Research Papers Yizhuo Zhai University of California at Riverside, USA, Yu Hao University of California at Riverside, USA, Hang Zhang University of California at Riverside, USA, Daimeng Wang University of California at Riverside, USA, Chengyu Song University of California at Riverside, USA, Zhiyun Qian University of California at Riverside, USA, Mohsen Lesani University of California at Riverside, USA, Srikanth V. Krishnamurthy University of California at Riverside, USA, Paul Yu U.S. Army Research Laboratory, USA DOI | ||
01:11 19mTalk | Conversations on Static Analysis Paper Presentations Daniil Tiganov University of Alberta, Canada, Haipeng Cai Washington State University, USA, Sahar Badihi University of British Columbia, Canada, Yizhuo Zhai University of California at Riverside, USA, M: Paul Gazzillo University of Central Florida | ||