The Impact of List Reduction for Language Agnostic Test Case Reducers
To find and fix bugs in compilers or in other code processing tools, modern language agnostic test case reducers boil the input files down to small bug-triggering versions. To do so they carefully craft lists of potentially irrelevant items and apply a list reduction to minimize them. We show that substituting the chosen list reduction algorithm improves the overall reducer runtime without affecting the final file sizes much. In a comparative study we combine 6 renown test case reducers with 7 established list reductions. Most renown reducers become faster by switching to another list reduction. We also present three ways to preprocess the crafted lists before the test case reducers pass them to the list reductions. We discuss the conditions for the preprocessings to improve the reducers’ speeds even more. On a benchmark of 321 C and SMT-LIB2 compiler bugs, selecting a different list reduction saves up to 74.7% of the runtime. Most test case reducers benefit from such a substitution. Preprocessing saves up to 9.1 additional percentage points. Combining these ideas saves up to 75.2% of the runtime.
Wed 2 AprDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:00 - 17:00 | Test Case Selection, Prioritisation, ReductionResearch Papers / Industry at Aula Magna (AM) Chair(s): Andrea Stocco Technical University of Munich, fortiss | ||
16:00 15mTalk | The Impact of List Reduction for Language Agnostic Test Case Reducers Research Papers Tobias Heineken Friedrich-Alexander-Universität Erlangen-Nürnberg, Michael Philippsen Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Programming Systems Group | ||
16:15 15mTalk | RustyRTS: Regression Test Selection for Rust Research Papers Simon Hundsdorfer Technical University of Munich, Roland Würsching Technical University of Munich, Alexander Pretschner TU Munich | ||
16:30 15mTalk | ML-Based Test Case Prioritization: A Research and Production Perspective in CI Environments Industry Md Asif Khan Ontario Tech University, Akramul Azim Ontario Tech University, Ramiro Liscano Ontario Tech University, Kevin Smith International Business Machines Corporation (IBM), Yee-Kang Chang International Business Machines Corporation (IBM), Gkerta Seferi International Business Machines Corporation (IBM), Qasim Tauseef International Business Machines Corporation (IBM) | ||
16:45 15mTalk | Evaluating Machine Learning-Based Test Case Prioritization in the Real World: An Experiment with SAP HANA Industry |