Djuradj Babic

Florida International University School of Computing and Information Sciences

Lecture Information:
  • November 9, 2012
  • 2:00 PM
  • ECS: 243

Speaker Bio

Mr. Djuradj Babic has a MasterÌ¢‰â‰ã¢s degree in Computer Science and is an Assistant Professor and the Hialeah Campus Department Chairperson at Miami Dade CollegeÌ¢‰â‰ã¢s School of Engineering and Technology. Prior to that, he served in a variety of positions, including teaching as an adjunct instructor at Florida International University and Broward College as well as teaching for the Miami Dade County Public Schools system. Mr. Babic has many years of expertise in pedagogy relating to the instruction of computer science courses such as Introduction to C++ Programming, Intermediate Programming in Java, Advanced Programming in C++, Advanced Java Programming, and Data Analysis. His teaching is enriched by instructional design that draws on disciplinary and learning scholarship and educational research.


As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial.

Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.