Lightweight Automatic Error Detection by Monitoring Collar Variables - Testing Software and Systems Access content directly
Conference Papers Year : 2012

Lightweight Automatic Error Detection by Monitoring Collar Variables

João Santos
  • Function : Author
  • PersonId : 1003384
Rui Abreu
  • Function : Author
  • PersonId : 1001783


Although proven to be an effective way for detecting errors, generic program invariants (also known as fault screeners) entail a considerable runtime overhead, rendering them not useful in practice. This paper studies the impact of using simple variable patterns to detect the so-called system’s collar variables to reduce the number of variables to be monitored (instrumented). Two different patterns were investigated to determine which variables to monitor. The first pattern finds variables whose value increase or decrease at regular intervals and deems them not important to monitor. The other pattern verifies the range of a variable per (successful) execution. If the range is constant across executions, then the variable is not monitored. Experiments were conducted on three different real-world applications to evaluate the reduction achieved on the number of variables monitored and determine the quality of the error detection. Results show a reduction of 52.04% on average in the number of monitored variables, while still maintaining a good detection rate with only 3.21% of executions detecting non-existing errors (false positives) and 5.26% not detecting an existing error (false negatives).
Fichier principal
Vignette du fichier
978-3-642-34691-0_16_Chapter.pdf (300.97 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01482403 , version 1 (03-03-2017)





João Santos, Rui Abreu. Lightweight Automatic Error Detection by Monitoring Collar Variables. 24th International Conference on Testing Software and Systems (ICTSS), Nov 2012, Aalborg, Denmark. pp.215-230, ⟨10.1007/978-3-642-34691-0_16⟩. ⟨hal-01482403⟩
49 View
49 Download



Gmail Facebook X LinkedIn More