Using the language independent genetic improvement tool MAGPIE (Machine Automated General Performance Improvement via Evolution of software), we measure the impact of GI on a non-deterministic deeply nested PARSEC VIPS parallel computing multi-threaded image processing benchmark written in C. More than 53% of mutants compile and generate identical output. Overall we find approximately 10% Failed Disruption Propagation (FDP). Excluding software detected internal errors and asserts, almost all changes deeper than 30 nested functions which are Executed and Infect data or change control are not Propagated. That is, almost all deep PIE code changes have no external impact. Suggesting (where it relies on testing) automatic software engineering on deeply nested code will be hard.
Tue 16 AprDisplayed time zone: Lisbon change
11:00 - 12:30 | Presentation Session 1GI@ICSE at Vianna da Motta Chair(s): Gabin An Korea Advanced Institute of Science and Technology, Justyna Petke University College London | ||
11:00 30mTalk | Deep Mutations have Little Impact GI@ICSE William Langdon University College London | ||
11:30 30mTalk | Grammar evolution and symbolic regression for astrometric centering of Hubble Space Telescope images GI@ICSE R. Sarmiento Universidad Internacional de la Rioja (UNIR), Spain, M. de la Cruz Universidad Internacional de la Rioja (UNIR), Spain, A. Ortega Universidad Internacional de la Rioja (UNIR), Spain, R. Baena-Galle Universidad Internacional de la Rioja (UNIR), Spain, T.M. Girard Southern Connecticut State University, USA, D.I. Casetti-Dinescu Southern Connecticut State University, USA, A. Cervantes Universidad Internacional de la Rioja (UNIR), Spain | ||
12:00 15mTalk | Genetic Improvement for DNN Security GI@ICSE Hunter Baxter Vanderbilt University, Yu Huang Vanderbilt University, Kevin Leach Vanderbilt University | ||
12:15 15mOther | Discussion GI@ICSE | ||
