Using Machine Learning to Build Test Oracles: an Industrial Case Study on Elevators Dispatching Algorithms
The software of elevators requires maintenance over several years to deal with new functionality, correction of bugs or legislation changes. To automatically validate this software, test oracles are necessary. A typical approach in industry is to use regression oracles. These oracles have to execute the test input both, in the software version under test and in a previous software version. This practice has several issues when using simulation to test elevators dispatching algorithms at system level. These issues include a long test execution time and the impossibility of re-using test oracles both at different test levels and in operation. To deal with these issues, we propose DARIO, a test oracle that relies on regression learning algorithms to predict the Qualify of Service of the system. The regression learning algorithms of this oracle are trained by using data from previously tested versions. An empirical evaluation with an industrial case study demonstrates the feasibility of using our approach in practice. A total of five regression learning algorithms were validated, showing that the regression tree algorithm performed best. For the regression tree algorithm, the accuracy when predicting verdicts by DARIO ranged between 79 to 87%.
Fri 21 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:30 - 18:15 | Industrial case studies & Doctoral StudentsAST 2021 at AST Room Chair(s): Breno Miranda Federal University of Pernambuco | ||
16:30 15mShort-paper | Continuous Testing Improvement Model AST 2021 Maximiliano Agustin Mascheroni Universidad Nacional de La Plata, Emanuel Agustin Irrazábal Universidad Nacional del Nordeste, Gustavo Rossi Universidad Nacional de La Plata, LIFIA-Fac. Informatica, La Plata, Argentina Pre-print Media Attached | ||
16:45 30mLong-paper | Model-based Automation of Test Scripts Generation Across Product Variants: a Railway Perspective AST 2021 Alessio Bucaioni Mälardalen University, Fabio Di Silvestro Bombardier Railway Transportation, Inderjeet Singh Bombardier Railway Transportation, Mehrdad Saadatmand RISE Research Institutes of Sweden, Henry Muccini University of L'Aquila, Italy, Thorvaldur Jochumsson Bombardier Railway Transportation Pre-print Media Attached | ||
17:15 30mLong-paper | Using Machine Learning to Build Test Oracles: an Industrial Case Study on Elevators Dispatching Algorithms AST 2021 Aitor Arrieta University of Mondragon, Jon Ayerdi Mondragon Unibertsitatea, Miren Illarramendi Mondragon University, Aitor Agirre IKERLAN-IK4, Goiuria Sagardui University of Mondragon
, Maite Arratibel Orona Pre-print Media Attached | ||
17:45 30mLong-paper | Automatically Assessing and Extending Code Coverage for NPM Packages AST 2021 Haiyang Sun Università della Svizzera italiana, Andrea Rosà University of Lugano, Switzerland, Daniele Bonetta Oracle Labs, Walter Binder University of Lugano, Switzerland Media Attached |
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