The challenge of automatically determining the correctness of test executions is referred to as the "test oracle problem" and is one of the
key remaining issues for automated testing. In the first part of this talk, I will present a means to solve the test oracle problem in a way that is general, scalable and accurate.
Our approach uses supervised learning over test execution traces.
In the second part of the talk, I will present testing challenges for Graphics Processing Units (GPUs).
GPUs are massively parallel processors offering performance acceleration and energy efficiency unmatched by current processors (CPUs) in computers. These advantages along with recent
advances in the programmability of GPUs have made them attractive for general-purpose computations. Despite the advances in programmability, GPU kernels are hard to code and analyse due to the high complexity of memory sharing patterns, striding patterns for memory accesses, implicit synchronisation, and combinatorial explosion of thread interleavings. We propose a testing technique for OpenCL kernels that combines mutation-based fuzzing and selective constraint solving with the goal of being fast, effective and scalable.
Dr.Ajitha Rajan is an Associate Professor in the School of Informatics at the University of Edinburgh, where she started in 2013 and was awarded the University Chancellor's Fellowship. She graduated with a PhD in Computer Science from the University of Minnesota in 2008, under the supervision of Prof. Mats Heimdahl. Dr. Rajan's research interests are in the area of software testing and verification. She has worked on different aspects of test automation -- test input generation, monitoring and test adequacy measurement, test execution, and test oracles. Her work has been published at various international conferences in software engineering and testing. She received the ACM Distinguished Paper award at ICSE 2008. Dr. Rajan has served as a PC member in many prestigious academic conferences, including ICSE, ISSTA, ASE, ECOOP, FMCAD. She is also the Associate editor for IET Software Journal. Dr. Rajan has been awarded grants from EPSRC, Facebook, GCHQ, Huawei, and SICSA. She was recently selected as one of the winners of the Facebook testing and Verification Award.