Last update: Sun Aug 5 02:03:07 MDT 2018
@Article{Anonymous:2000:AES,
author = "Anonymous",
title = "Assessing and enhancing software testing
effectiveness",
journal = j-SIGSOFT,
volume = "25",
number = "1",
pages = "50--51",
month = jan,
year = "2000",
CODEN = "SFENDP",
DOI = "https://doi.org/10.1145/340855.340888",
ISSN = "0163-5948 (print), 1943-5843 (electronic)",
ISSN-L = "0163-5948",
bibdate = "Wed Aug 1 17:13:50 MDT 2018",
bibsource = "http://www.math.utah.edu/pub/tex/bib/sigsoft2000.bib",
abstract = "Although many techniques for testing software have
been proposed over the last twenty years, there is
still not enough solid evidence to indicate which (if
any) of these techniques are effective. It is difficult
to perform meaningful comparisons of the cost and
effectiveness of testing techniques; in fact, even
defining these terms in a meaningful way is
problematic. Consider an erroneous program P, its
specification S, and a test data adequacy criterion C
(such as 100\% branch coverage). Even if we restrict
the size of the test sets to be considered, there are a
huge number of different test sets that satisfy
criterion C for P and S. Since these adequate test sets
typically have different properties, in order to
investigate effectiveness (or other properties)
rigorously, the entire space of test sets must be
considered (according to some reasonable probability
distribution) and appropriate probabilistic analysis
and/or statistical sampling techniques must be used. In
earlier research, supported by NSF Grant CCR-9206910,
we developed analytical tools and an experiment design
to address these issues and applied them to comparing a
number of well-known testing techniques. The primary
measure of effectiveness considered was probability
that an adequate test set would detect at least one
fault and the most of the experiment subjects were
fairly small. The main thread of this research project
extends that work in several directions: additional
measures of cost and effectiveness are considered,
analytical and experimental tools are developed for
these measures, and experiments are conducted on larger
programs.",
acknowledgement = ack-nhfb,
fjournal = "ACM SIGSOFT Software Engineering Notes",
journal-URL = "https://dl.acm.org/citation.cfm?id=J728",
}