You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.
The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.
For the reports in this repository we specifically note that
- the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
- the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
- technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
- in other cases, please contact the copyright owner for detailed information
By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.
If you are in doubt, feel free to contact webmaster@ide.mdh.se
Resource Constrained Test Case Prioritization with Simulated Annealing in an Industrial Context
Publication Type:
Conference/Workshop Paper
Venue:
The 39th ACM/SIGAPP Symposium On Applied Computing
Abstract
We need to find an effective prioritization of regression test cases due to their growing number. This may happen on parallel test systems and software branches. We compared regression test pri- oritization approaches against several goals of importance in an industrial context. We experimentally compared different simulated annealing approaches, hypothetical ideal and worst prioritizations, as well as reference prioritizations such as random, historical failure rate, age, etc. These were evaluated against a heuristic metric that combines several factors, as well as reference metrics such as failure count, days since last execution, etc. By simulating resource star- vation in terms of available time, we found that some approaches rapidly degraded, e.g., by only prioritizing recently failed tests, the average number of nights since last execution was about five times as bad as for a random selection. The simulated annealing approach with large search space and many iterations came out best for many metrics. Interestingly, the poorest prioritization was achieved by aiming at diversity, and the coverage-based prioritization was poor at finding failures.
Bibtex
@inproceedings{Felding6848,
author = {Eric Felding and Per Erik Strandberg and Nils-Hassan Quttineh and Wasif Afzal},
title = {Resource Constrained Test Case Prioritization with Simulated Annealing in an Industrial Context},
month = {January},
year = {2024},
booktitle = {The 39th ACM/SIGAPP Symposium On Applied Computing},
url = {http://www.es.mdu.se/publications/6848-}
}