My Journey to today: 1981 Started Grad School Late 1985 Married Mark

1. Lori PollockProfessorProgram Analysis, Software Development/Maintenance Tools, Optimizing Compilers My Journey to today: 1981 Started Grad School Late 1985 Married Mark Early 1986 PhD in CS at University of Pittsburgh 1988 Lauren was born 1986-1990 Assistant Professor, Rice University, Houston 1990 Lindsay was born 1991-present Assistant, Associate, Full Professor UD CIS 1995 Matt was born

2. What I do here at UD • Research • Software Engineering and Compilation Lab (Hiperspace) • 213 Smith Hall • Collaborations • Vijay Shanker (UD CIS), Lisa Marvel (Army Research Lab), Martin Swany (UD CIS), John Cavazos (UD CIS), Guang Gao (UD ECE) • Funding • Several NSF grants; previously Army funding • Graduate Teaching • CISC 672 Compilers • CISC 872 Program Analysis and Transformations • CISC 879 Software Testing and Maintenance • CISC 879 Software Tools and Environments

3. What I do outside UD • Cochair, Computing Research Association (CRA)’s Committee on the Status of Women in Computer Research (CRA-W) • Mentoring – speaker at mentoring workshops for undergrads, grads, assistant and associate profs, and industry lab researchers • Program committees, conference organization, NSF panels, paper reviews,… (typical of university researchers)

4. Hiperspace Lab Grad StudentsUniversity of Delaware Antony Danalis PhD Ben Breech PhD Emily Gibson PhD Giri Sridhara PhD Kishen Maloor Masters Thesis

5. Just Completed PhD in 2007 Sara Sprenkle Assistant Prof Washington & Lee U David Shepherd Postdoc University of British Columbia, Canada Mike Jochen Assistant Prof East Stroudsburg U

6. Research in Hiperspace Natural Language Analysis of Programs Optimization of Cluster Parallel Programs Dave, Emily, Giri, Kishen Antony Program Analysis Compiler Technology Ben Testing Web Applications Runtime Test Generation via Dynamic Compilers Mike Sreedevi, Sara, Emily Mobile Code Security Software Engineering…………..Security…..........Compilers….……Parallel Computing

7. Research Problem How can scientific codes be scaled to large numbers of CPUs? Communication impedes scalability ASPhALT: Automatic System for Parallel AppLication Transformation Approach Hide communication latency Collect cluster “knowledge” Compiler analyze dependencies Asynch I/O harness overlap Integrated system to optimize communication Contribution - First to “cluster-optimize” existing MPI codes

8. Dynamic Compiler Executing Program Input Analysis Results RUGRAT: RUntime GeneRAtion of Tests with Dynamic Compilers Dynamic compilers: perform analysis and code modification at runtime Research Question: How can dynamic compilation technology be exploited for use beyond program optimization? Approach:Extend to analyze for: Software engineering tasks – impact analysis Program security mechanism and error handling testing

9. Server Server Network Network Clients Clients SECRYT – StEgo-CRYpto Tamper detection Research Problem • Mobile code, particularly dynamically evolving mobile code can greatly enhance current computing models • How can we validate or control dynamically evolving mobile programs in an efficient way? Proposed Approach • Embed static Tamper Detection Marks (SECRYT) • Control how the program dynamically evolves (DOCTORS) Contribution • Enable efficient mobile code tamper detection via steganography • Enable distributed dynamic program transformation technologies in a safe, efficient manner

10. Maintenance Testing for Web Applications Research Problem: How can we exploit user session logging for testing of web applications after initial deployment, with minimal tester effort? Contribution: Scalable, cost-effective, practical, automated structural testing framework for web applications Web Application (v.1.0) Beta Web Application (v.0.9) Deployment Record field data field data Test v.1.0 with field data Users access Collect, cluster, reduce user session data on-the-fly and automate testing process Approach:

11. Natural Language-based Analysis of Software Research Problem - 60-90% software costs are in reading and navigating large software systems to fix bugs and add new features. - Programmers leave clues of their intent as they choose names. • Focus on actions • Correspond to verbs • Verbs need Direct Object Proposed Approach • Develop, extend, and apply natural language-based analysis of programmer-given names and comments and use in client tools Contribution - Aid understanding, debugging, maintenance, development

12. Clients of NLPA Thus Far • FindConcept: Search Tool • Timna: Aspect Miner • Dora the Explorer: Program Explorer given a seed • NL technology used Synonyms, collocations, morphology, word frequencies, part-of-speech tagging, AOIG • Evaluation indicates Natural language information shows real promise for taking software development tools to the next level of effectiveness • Key to success Accurate extraction of NL clues

13. Current Research in Hiperspace Natural Language Analysis of Code Optimization of Cluster Parallel Programs Program Analysis Compiler Technology Runtime Test Generation via Dynamic Compilers Natural Language Analysis of comments Software Engineering…………..Security…..........Compilers….……Parallel Computing

14. Hiperspace Goals for Students • Learn how to identify , formulate, and address important open problems • Publish in high quality conferences • Gain teaching experience • Actively participate in post-graduation activities • Develop self confidence and independence • Build a professional/peer community - And, have fun!