Testing at Google poses many challenges: release cycle is extremely short and there exists such a heavy layer of dependency that you may break someone else's product without knowing. Dr. Shin Yoo has been collaborating with Google to release a Google-internal tool, based on multi-objective test suite optimisation, that enables the developers to get earlier fault feedback before submitting their code changes. The project was funded by Google Research Award and the result has been invited to be presented at Google Test Automation Conference (GTAC) 2010 in India as well as in ESEC/FSE 2011.

- Faster Fault Finding at Google Using Multi Objective Regression Test Optimisation, Shin Yoo, Robert Nilsson and Mark Harman, 8th European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE '11) [pdf] [bibtex]

Microsoft Research developed an automated white-box test data generation tool called Pex, which is now part of MS Visual Studio 2010 Power Tools. Pex is based on Dynamic Symbolic Execution (DSE) of source code. While this approach is very powerful, there are certain limitations that prevent Pex to achieve higher code coverage. Dr. Kiran Lakhotia collaborated with Microsoft Research and developed an extension for Pex that uses SBST to overcome those limitations.

- FloPSy - Search-Based Floating Point Constraint Solving for Symbolic Execution, Kiran Lakhotia, Nikolai Tillmann, Mark Harman and Jonathan de Halleux, 22nd IFIP International Conference on Testing Software and Systems, pages 142-157 [pdf] [bibtex]

Smoke-testing IBM's middleware products require the tester to come up with different sets of test that can be executed within a very short time (typically 1 hour). Dr. Shin Yoo's collaboration with IBM Haifa led to the use of graphics card to parallelise multi-objective test suite optimisation algorithm. Sequential algorithm took more than 1 hour to optimise IBM's test data, which defies the aim of smoke testing. The parallelised algorithm using General Purpose computation on Graphics Processing Unit (GPGPU) brought in 25x speedup and reduced the optimisation time down to less than 3 minutes.

- "Highly Scalable Multi-Objective Test Suite Minimisation Using Graphics Cards", Shin Yoo and Shmuel Ur, Proceedings of the 3rd International Symposium on Search Based Software Engineering (SSBSE '11) [pdf] [bibtex]

ABB is not only a world-leading manufacturer of industrial robots but also a supplier of robots used in the latest Terminator movies. Yue Jia has been collaborating with ABB to improve the quality of ABB's robot testing by adopting mutation testing.

-Constructing Subtle Faults Using Higher Order Mutation Testing, Yue Jia and Mark Harman, Proceedings of the 8th International Working Conference on Source Code Analysis and Manipulation (SCAM '08), pages 249-258 (Best Paper Award) [pdf] [bibtex]