Papers on research funded by the
ABB
Software Architecture and Processes
(SWAP) program in Corporate Research
"An Initial Study of a Lightweight Process for Change Identification
and Regression Test Selection When Source Code is Not Available"
Presented at
Karen Smiley (ABB).
Abstract:
Various regression test selection techniques have
been developed and have shown to improve testing cost
effectiveness via improving efficiency. The majority of
these test selection techniques rely on access to source
code for change identification. However, when new
releases of COTS components are made available for
integration and testing, source code is often not
available to guide in regression test selection. In this
paper we describe a lightweight Integrated - Black-box
Approach for Component Change Identification (I-BACCI)
process for selection of regression tests for
user/glue code that uses COTS components. I-BACCI
is applicable when component licensing agreements do
not preclude binary code analysis. A case study of the
process was conducted on an ABB product that uses a
medium-scale internal ABB software component. Six
releases of the component were examined to evaluate
the efficacy of the proposed process. The result of the
case study indicates that this process can reduce the
required regression tests by 40% on average.