Applying and Extending the Delta Debugging Algorithm for Elevator Dispatching Algorithms (Experience Paper)
Pablo Valle, Aitor Arrieta, Maite Arratibel
Abstract
Elevator systems are one kind of Cyber-Physical Systems (CPSs), and as such, test cases are usually complex and long in time. This is mainly because realistic test scenarios are employed (e.g., for testing elevator dispatching algorithms, typically a full day of passengers traveling through a system of elevators is used). However, in such a context, when needing to reproduce a failure, it is of high benefit to provide the minimal test input to the software developers. This way, analyzing and trying to localize the root-cause of the failure is easier and more agile. Delta debugging has been found to be an efficient technique to reduce failure-inducing test inputs. In this paper, we enhance this technique by first monitoring the environment at which the CPS operates as well as its physical states. With the monitored information, we search for stable states of the CPS during the execution of the simulation. In a second step, we use such identified stable states to help the delta debugging algorithm isolate the failure-inducing test inputs more efficiently. We report our experience of applying our approach into an industrial elevator dispatching algorithm. An empirical evaluation carried out with real operational data from a real installation of elevators suggests that the proposed environment-wise delta debugging algorithm is between 1.3 to 1.8 times faster than the traditional delta debugging, while producing a larger reduction in the failure-inducing test inputs. The results provided by the different implemented delta debugging algorithm versions are qualitatively assessed with domain experts. This assessment provides new insights and lessons learned, such as, potential applications of the delta debugging algorithm beyond debugging. CCS CONCEPTS • Software and its engineering → Software performance; Empirical software validation; Software testing and debugging; • Computer systems organization → Embedded systems.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Builds on9
- Misbehaviour prediction for autonomous driving systemsAndrea Stocco, Michael Weiss, Marco Calzana, Paolo TonellaICSE 2020 · 138 citations
- A comprehensive study of autonomous vehicle bugsJoshua Garcia, Yang Feng, Junjie Shen, Sumaya Almanee et al.ICSE 2020 · 127 citations
- An exploratory study of autopilot software bugs in unmanned aerial vehiclesDinghua Wang, Shuqing Li, Guanping Xiao, Yepang Liu et al.FSE 2021 · 60 citations
- Approximation-refinement testing of compute-intensive cyber-physical models: an approach based on system identificationClaudio Menghi, Shiva Nejati, Lionel C. Briand, Yago Isasi ParacheICSE 2020 · 59 citations
- Targeting Requirements Violations of Autonomous Driving Systems by Dynamic Evolutionary SearchYixing Luo, Xiao-Yi Zhang, Paolo Arcaini, Zhi Jin et al.ASE 2021 · 38 citations
Related papers
- FIGCPS: Effective Failure-inducing Input Generation for Cyber-Physical Systems with Deep Reinforcement LearningShaohua Zhang, Shuang Liu, Jun Sun, Yuqi Chen et al.ASE 2021 · 13 citations
- Probabilistic Delta debuggingGuancheng Wang, Ruobing Shen, Junjie Chen, Yingfei Xiong et al.FSE 2021 · 56 citations
- Catch Me If You Learn: Real-Time Attack Detection and Mitigation in Learning Enabled CPSIpsita Koley, Sunandan Adhikary, Soumyajit DeyRTSS 2021 · 8 citations
- Finding Causally Different Tests for an Industrial Control SystemChristopher M. Poskitt, Yuqi Chen, Jun Sun, Yu JiangICSE 2023 · 6 citations
- Structure-Aware Delta Debugging with Geometric-Information WeightsYonggang Tao, Jingling XueFSE 2026
