ICST 2023
Sun 16 - Thu 20 April 2023 Dublin, Ireland
Tue 18 Apr 2023 14:20 - 14:40 at Pearse suite - Session 11: Test Generation Chair(s): Gregory Gay

Conventional automated test case generation techniques do not scale to modern software systems, as these systems have a large number of requirements that change frequently. In this paper, we present a scalable algorithm, AGenT, that generates test cases to cover maximal requirements. AGenT takes Expressive Decision Tables (EDT), specifying requirements of a system, as input and realises these as multiple Discrete Time Automata (DTAs). AGenT then generates test cases to cover each row of the tables. To improve scalability, it attempts to cover nearer rows (requiring fewer inputs) first, where distance is measured using a novel distance-to-match heuristic. It also maintains information about desirability and predictability of inputs so as to select promising inputs with a higher probability. Although the algorithm has been presented in the context of EDT, it operates on its DTA representation and hence can be applied to any system that is represented as a collection of DTAs like Statemate and Stateflow. In this paper, we describe AGenT in detail and present findings from two experiments that we conducted. We compared AGenT with state-of-the-art algorithms, DRAFT and a random test case generation algorithm, RTG. In the first experiment, AGenT took a maximum of 144 seconds to cover all rows whereas the other two algorithms timed out on many modules. In the second experiment, for a module with 701 rows, AGenT achieved 7% more coverage than DRAFT and 12% more than RTG.

Tue 18 Apr

Displayed time zone: Dublin change

14:00 - 15:30
Session 11: Test GenerationJournal-First Papers / Previous Editions / Research Papers / Tool Demo at Pearse suite
Chair(s): Gregory Gay Chalmers | University of Gothenburg
Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study
Journal-First Papers
Jannik Fischbach Netlight GmbH / fortiss GmbH, Julian Frattini Blekinge Institute of Technology, Andreas Vogelsang University of Cologne, Daniel Mendez Blekinge Institute of Technology, Michael Unterkalmsteiner Blekinge Institute of Technology, Andreas Wehrle Allianz Deutschland AG, Pablo Restrepo Henao Technical University of Munich, Parisa Yousefi Ericsson, Tedi Juricic Ericsson, Jeannette Radduenz Allianz Deutschland, Carsten Wiecher Kostal Automobil Elektrik GmbH & Co. KG
Scaling Test Case Generation For Expressive Decision Tables
Previous Editions
Supriya Agrawal Tata Consultancy Services Ltd. (TCS), R. Venkatesh , Ulka Shrotri Tata Consultancy Services Ltd. (TCS), Amey Zare TCS Research, Sagar Verma Tata Consultancy Services Ltd. (TCS)
Spectacular: Finding Laws from 25 Trillion Programs
Research Papers
Matthías Páll Gissurarson Chalmers University of Technology, Sweden, Diego Roque Dark Forest Technologies, James Koppel Massachusetts Institute of Technology, USA
Pairwise Testing Revisited for Structured Data With Constraints
Research Papers
Luca V. Sartori LAAS-CNRS, Helene Waeselynck LAAS-CNRS, Jérémie Guiochet LAAS-CNRS
RICK: Generating Mocks from Production Data
Tool Demo
Deepika Tiwari KTH Royal Institute of Technology, Martin Monperrus KTH Royal Institute of Technology, Benoit Baudry KTH