Abstract:
Software engineering researchers acquire knowledge about programs by measuring relevant statistics, reasoning about observed phenomena, and predicting the behavior of the programs. Each program exhibits certain unique characteristics during analysis. Though traditional statistical methods provide a rigorous analysis of data, they are rather generic and lack the flexibility to build a unique representation for each program. Bayesian methods for data analysis, on the other hand, allow for flexible updates of the knowledge acquired through observations. Bayesian formulations for research challenges in software engineering would therefore enable researchers to make significant contributions to this field.
Despite their strong mathematical basis and obvious suitability to software analysis, Bayesian methods are still largely under-utilized in the software engineering community. This tutorial aims to provide a broad introduction of Bayesian methods for data analysis, with a specific focus on problems of interest to the software testing community. In addition, the tutorial will provide an in-depth understanding of selected relevant topics such as Bayesian inference, probabilistic prediction techniques, Markov models, information theory and stochastic sampling. The core concepts will be explained using case studies and the application of prominent statistical tools on examples drawn from the field of software testing. A brief comparison with popular methods for modeling uncertain data such as neural networks and regression will be provided. At the end of the tutorial, the participants will be equipped with the necessary skills and background knowledge to formulate their research problems using Bayesian methods, and analyze their formulation using appropriate statistical tools.
Presenters:
Mohan Sridharan is an Assistant Professor of computer science at Texas Tech University. Prior to his current appointment, he was a Research Fellow in the School of Computer Science at the University of Birmingham (UK). He received his PhD in Electrical and Computer Engineering from The University of Texas at Austin. His research interests include robotics, statistical machine learning, computer vision and autonomous multiagent systems. Dr. Sridharan has spent more than seven years using Bayesian techniques to solve challenging problems in the field of robot vision. His recent research on probabilistic planning won a Distinguished Paper Award at ICAPS-08, a premier planning conference. Furthermore, he has designed and taught multiple courses on statistical machine learning and probabilistic robotics.
Akbar Siami Namin is an Assistant Professor of computer science at Texas Tech University. He received his PhD in Computer Science from the University of Western Ontario, Canada. He leads the AdVanced Empirical Software Testing and Analysis (AVESTA) research group at Texas Tech University. His research interests include software testing and program analysis, empirical software engineering, and statistical data analysis. He has designed and taught several tutorials, undergraduate courses and graduate seminars at the University of Western Ontario, London, Canada, and Texas Tech University, USA. Examples of such courses include Software Testing and Analysis, An Introduction to Computer Science, Software Verification and Validation, and Automated Software Testing.