Regression Test Selection for Black-box Dynamic Link Library Components

  • Authors:

  • Jiang Zheng;Laurie Williams;Brian Robinson;Karen Smiley

  • Affiliations:

  • North Carolina State University, Raleigh;North Carolina State University, Raleigh;ABB Inc., US Corporate Research;ABB Inc., US Corporate Research

  • Venue:
  • IWICSS '07 Proceedings of the Second International Workshop on Incorporating COTS Software into Software Systems: Tools and Techniques
  • Year:
  • 2007

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Abstract

Software products are often configured with commercial-off-the-shelf (COTS) components. When new releases of these components are made available for integration and testing, source code is usually not provided. Various regression test selection processes have been developed and have been shown to be cost effective. However, the majority of these test selection techniques rely on access to source code for change identification. Based on our prior work, we are studying the solution to regression testing COTS-based applications that incorporate components of dynamic link library (DLL) files. We evolved the Integrated - Black-box Approach for Component Change Identification (I-BACCI) process that selects regression tests for applications based upon static binary code analysis to Version 4 to support DLL components. A feasibility case study was conducted at ABB on products written in C/C++ to determine the effectiveness of the I-BACCI process. The results of the case study indicate this process can reduce the required number of regression tests by as much as 100% if our analysis indicates the changes to the component are not called by the glue code of the application using the COTS component. Similar to other regression test selection techniques, when there are many changes in the new component I-BACCI suggests a retest-all regression test strategy.