PReach: A Heuristic for Probabilistic Reachability to Identify Hard to Reach Statements
Fri 13 May 2022 11:10 - 11:15 at ICSE room 1-odd hours - Reliability and Safety 6 Chair(s): Pasqualina Potena
Wed 25 May 2022 09:40 - 09:45 at Room 304+305 - Papers 3: Reliability and Safety Chair(s): Cristian Cadar
Wed 25 May 2022 13:30 - 15:00 at Ballroom Gallery - Posters 1
We present a heuristic for approximating the likelihood of reaching a given program statement using 1) branch selectivity (representing the percentage of values that satisfy a branch condition), which we compute using model counting, 2) dependency analysis, which we use to identify input-dependent branch conditions that influence statement reachability, 3) abstract interpretation, which we use to identify the set of values that reach a branch condition, and 4) a discrete-time Markov chain model, which we construct to capture the control flow structure of the program together with the selectivity of each branch. Our experiments indicate that our heuristic-based probabilistic reachability analysis tool PReach can identify hard to reach statements with high precision and accuracy in benchmarks from software verification and testing competitions, Apache Commons Lang, and the DARPA STAC program. We provide a detailed comparison with probabilistic symbolic execution and statistical symbolic execution for the purpose of identifying hard to reach statements. PReach achieves comparable precision and accuracy for bounded execution depth and better precision and accuracy when execution depth is unbounded and the number of program paths grows exponentially. Moreover, PReach is more scalable than both probabilistic and statistical symbolic execution.
Mon 9 MayDisplayed time zone: Eastern Time (US & Canada) change
20:00 - 21:00 | Reliability and Safety 3Technical Track at ICSE room 3-even hours Chair(s): Antonio Filieri Imperial College London | ||
20:00 5mTalk | Promal: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning Technical Track Changlin Liu Case Western Reserve University, Hanlin Wang Case Western Reserve University, Tianming Liu Monash Univerisity, Diandian Gu Peking University, Yun Ma Peking University, Haoyu Wang Huazhong University of Science and Technology, China, Xusheng Xiao Case Western Reserve University DOI Pre-print Media Attached | ||
20:05 5mTalk | EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries Technical Track Jiannan Wang Purdue University, Thibaud Lutellier University of Waterloo, Shangshu Qian Purdue University, Hung Viet Pham University of Waterloo, Lin Tan Purdue University Pre-print Media Attached | ||
20:10 5mTalk | DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning Technical Track Chenxi Zhang Fudan University, Xin Peng Fudan University, Chaofeng Sha Fudan University, Ke Zhang Fudan University, Zhenqing Fu Fudan University, Xiya Wu Fudan University, Qingwei Lin Microsoft Research, Dongmei Zhang Microsoft Research Pre-print Media Attached | ||
20:15 5mTalk | Repairing Brain-Computer Interfaces with Fault-based Data Acquisition Technical Track Cailin Winston University of Washington, Caleb Winston University of Washington, Chloe N Winston University of Washington, Claris Winston University of Washington, Cleah Winston , Rajesh PN Rao University of Washington, René Just University of Washington Pre-print Media Attached | ||
20:20 5mTalk | PReach: A Heuristic for Probabilistic Reachability to Identify Hard to Reach Statements Technical Track Seemanta Saha University of California Santa Barbara, Mara Downing University of California, Santa Barbara, Tegan Brennan , Tevfik Bultan University of California, Santa Barbara Pre-print Media Attached |