Search-Based Scheduling of Experiments in Continuous Deployment
Paper i proceeding, 2018

Continuous experimentation involves practices for testing new functionality on a small fraction of the user base in production environments. Running multiple experiments in parallel requires handling user assignments (i.e., which users are part of which experiments) carefully as experiments might overlap and influence each other. Furthermore, experiments are prone to change, get canceled, or are adjusted and restarted, and new ones are added regularly. We formulate this as an optimization problem, fostering the parallel execution of experiments and making sure that enough data is collected for every experiment avoiding overlapping experiments. We propose a genetic algorithm that is capable of (re-)scheduling experiments and compare with other search-based approaches (random sampling, local search, and simulated annealing). Our evaluation shows that our genetic implementation outperforms the other approaches by up to 19% regarding the fitness of the solutions identified and up to a factor three in execution time in our evaluation scenarios.

Release engineering

Scheduling

Continuous experimentation

Search based software engineering

Författare

Gerald Schermann

Universität Zürich

Philipp Leitner

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for People, Architecture, Requirements and Traceability

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

485-495 8530054

2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018
Madrid, Japan,

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.1109/ICSME.2018.00059

Mer information

Senast uppdaterat

2019-01-18