Laurie Williams

Laurie Williams is a Distinguished University Professor in the Computer Science Department of the College of Engineering at North Carolina State University (NCSU). Laurie is a co-director of the NCSU Science of Security Lablet sponsored by the National Security Agency, the NCSU Secure Computing Institute, and is the Principal Cybersecurity Technologist of the SecureAmerica Institute. Laurie’s research focuses on software security; agile software development practices and processes, particularly continuous deployment; and software reliability, software testing and analysis. Laurie is an ACM and an IEEE Fellow.

Solving Software Security Challenges with Artificial Intelligence


Software security lies at the intersection of software engineering and cybersecurity  –  building security into a product.  Software security techniques focus on preventing the injection of vulnerabilities and detecting the vulnerabilities that make their way into a product or the deployment pipeline before the product is released.  Increasingly, artificial intelligence is being used to power software security techniques to aid organizations in deploying secure products.  This talk will present a landscape of research and practice at the intersection of software engineering, cybersecurity, and artificial intelligence to solve cybersecurity challenges.  The talk will also present research projects conducted by the speaker’s own research group. 

Leandro L. Minku

Leandro L. Minku is a Lecturer in Intelligent Systems at the School of Computer Science, University of Birmingham (UK). Dr. Minku’s main research interests are machine learning in non-stationary environments / data stream mining, online class imbalance learning, ensembles of learning machines and computational intelligence for software engineering.

Prediction of Defect-Inducing Software Changes in Dynamic Environments


Software systems have become ever larger and more complex. This inevitably leads to software defects, which are costly to debug and fix. To help reducing the number of defects, machine learning approaches have been proposed for predicting defect-inducing changes in software source code at commit time. By inspecting such changes at commit time, developers can reduce the chances of inducing defects that are much more costly to debug and fix at later stages. While results in this field are promising, most existing work assumes that the characteristics of the problem remain the same over time. However, the environment where software projects are conducted is dynamic, being affected by changes in the development team, in the management strategy, in the current stage of the software development, among others. Such dynamism means that the defect generating process can suffer variations over time, impairing the ability of machine learning classifiers to perform well. In this talk, I will discuss the latest advancements in dealing with prediction of defect-inducing software changes in dynamic environments.

Carolyn Seaman

Carolyn Seaman is Associate Professor in the Information Systems Department at the University of Maryland Baltimore County (UMBC). Dr. Seaman’s main research interests is empirical studies of software engineering, with particular emphases on maintenance, organizational structure, communication, measurement, COTS-based development, and qualitative research methods.

Alessandro Garcia

Alessandro Garcia

Pontifical Catholic University of Rio de Janeiro, Brazil

Daniela Damian

University of Victoria, Canada