Faculty Talks

3:00 pm to 4:30 pm

Location: ISEB 1010

picture of Iftekhar Ahmed
"Explaining Misprediction of Machine Learning Models in Software Engineering"
Iftekhar Ahmed, Assistant Professor, Department of Informatic, UC Irvine

Interpreting and debugging machine learning models is necessary to ensure the robustness of the machine learning models. Explaining mispredictions can help significantly in doing so. While recent works on misprediction explanation have proven promising in generating interpretable explanations for mispredictions, the state-of-the-art techniques “blindly” deduce misprediction explanation rules from all data features, which may not be scalable depending on the number of features. I will present our research on alleviating this problem, which proposes an efficient misprediction explanation technique leveraging prior knowledge about data. I will also present our work reducing model’s bias by focusing on subsets of data during training that are more prone to being mispredicted.


Iftekhar Ahmed received his B.Sc. degree in Computer Science and Engineering from Shahjalal University of Science and Technology, Bangladesh. He received Ph.D. degree in Computer Science from Oregon State University. He is an Assistant Professor in the School of Information and Computer Sciences, University of California, Irvine, where he leads the Software Engineering & Testing Using Artificial Intelligence for Reliable Software (STAIRS) group. He is also a faculty member of the Institute for Software Research, UCI. His research interests include the field of software engineering, with a focus on combining testing, static analysis, socio-technical factors analysis, and machine learning approaches to help improve software quality under realworld conditions. His Ph.D. work on improving the effectiveness of mutation analysis for largescale real-world software systems has helped to identify several bugs in the Linux kernel. The improvements resulting from his work have been incorporated into the Linux distributions, with more than 2 Billion instances running worldwide from mobile phones to data centers. His recent work on test smells detection is implemented as a plugin for PyCharm, a popular IDE for Python developed by JetBrains with more than 10 million developers using it worldwide. He is a member of the ACM, the ACM SIGSOFT, and the IEEE.

For more information, please visit his webpage at: http://www.iftekharahmed.info

picture of Joshua Garcia
"Doppelgänger Test Generation for Revealing Bugs in Autonomous Driving Software"
Joshua Garcia, Assistant Professor, Department of Informatics, UC Irvine

Vehicles controlled by autonomous driving software (ADS) are expected to bring many social and economic benefits, but at the current stage not being broadly used due to concerns with regard to their safety. Virtual tests, where autonomous vehicles are tested in software simulation, are common practices because they are more efficient and safer compared to field operational tests. Specifically, search-based approaches are used to find particularly critical situations. These approaches provide an opportunity to automatically generate tests; however, systematically producing bug-revealing tests for ADS remains a major challenge. To address this challenge, we introduce DoppelTest, a test generation approach for ADSes that utilizes a genetic algorithm to discover bug-revealing violations by generating scenarios with multiple autonomous vehicles that account for traffic control (e.g., traffic signals and stop signs). Our extensive evaluation shows that DoppelTest can efficiently discover 123 bug-revealing violations for a production-grade ADS (Baidu Apollo), which we then classify into 8 unique bug categories.


Joshua Garcia is an assistant professor at the Donald Bren School of Information and Computer Sciences at the University of California, Irvine. His current research interests are in the area of software engineering and include smart system (e.g., mobile apps and autonomous driving systems) testing, analysis, and security—and addressing problems of software architectural drift and erosion. He received three degrees from the University of Southern California: a B.S. in computer engineering and computer science, an M.S. in computer science, and a Ph.D. in computer science. His research tools and datasets have been used by dozens of researchers, agencies, and companies around the world—including universities in Argentina, Australia, Brazil, Canada, China, Europe, and the United States, and by companies and government agencies such as Boeing, Bosch, Google, IBM, Microsoft, Northrop Grumman, the FBI, the Department of Homeland Security, and NASA.

picture of Cristina Videira Lopes
"Automatic Commit Message Generation"
Cristina Videira Lopes, Chancellor’s Professor, Department of Informatic, UC Irvine

Can a Large Language Model generate appropriate commit messages based solely on code diffs? This talk describes a study my students and I did on this topic, highlighting the potential, and some limitations, of LLMs for this particular software engineering task.


Cristina (Crista) Lopes is a Professor in the School of Computer Sciences at University of California, Irvine, with research interests in Programming Languages, Software Engineering, and Distributed Virtual Environments. She is an IEEE Fellow, an ACM Distinguished Scientist, a twice-elected member of the SIGPLAN Executive Committee, and founding Editor in Chief of The Art, Science, and Engineering of Programming. She is the recipient of the 2016 Pizzigati Prize for Software in the Public Interest for her work in the OpenSimulator virtual world platform.

picture of Mohammad Moshirpour
"Human Aspects of Software Engineering"
Mohammad Moshirpour, Associate Professor of Teaching, Department of Informatics, UC Irvine

Open-source software has become increasingly popular in industrial products with the advent of open-source software. Due to several open-source software alternatives for a particular purpose, industry practitioners must decide among multiple candidates for adoption. One of the primary sources of information about open-source software is its documentation. Despite this, there are no studies regarding the importance of documentation in OSS adoption decisions. Therefore, our first step is to investigate the role of this software artifact in the industry's adoption decision-making process. After learning how the industry uses documentation when adopting, we will use natural language processing (NLP) techniques to extract the industry's preferred information from the documentation.


Mohammad Moshirpour is the director of the Software Engineering Practice and Education (SEPE) Research Group. He is an associate professor of teaching at the Department of Informatics, Donald Bren School of Information & Computer Sciences at the University of California, Irvine. His research interests include software design and development, software requirements engineering, machine learning, and software engineering training and education. He is the recipient of several awards for his work in developing tools and methodologies for software engineering practices and education including the 2021 D2L Innovation Award.

picture of Lee Martie
"Rapid Development of Compositional AI "
Lee Martie, Senior Research Scientist and Manager, IBM

Compositional AI systems, which combine multiple artificial intelligence components together with other application components to solve a larger problem, have no known pattern of development and are often approached in a bespoke and ad hoc style. This makes development slower and harder to reuse for future applications. To support the full rapid development cycle of compositional AI applications, we have developed a novel framework called (Bee)* (written as a regular expression and pronounced as "beestar"). We illustrate how (Bee)* supports building integrated, scalable, and interactive compositional AI applications with a simplified developer experience.


Lee Martie is a tools researcher and practitioner of software engineering, where his focus has been shaped by his time in academia and industry. Lee received his degrees at the Georgia Institute of Technology and at the University of California, Irvine, where he received his PhD in software engineering. His work experience includes being a research scientist at the Georgia Institute of Technology, software engineer at Microsoft, and senior research scientist and manager at IBM Research’s MIT-IBM Watson AI Lab.

As a tool researcher, Lee’s area of research often sits at the intersection between software engineering, artificial intelligence, and user-centered design. Some examples of his work include integrated development environments for game playing agents, source code search engines, platforms to quickly (de)allocate GPUs and data for AI development, and, recently, a framework for rapid development of compositional AI applications.

In practice, Lee currently lead’s an engineering team of 10 individuals with skills in AI, frontend, backend, systems, and data science. The team’s mission is to apply research to create prototypes and to develop tools, AI models, and APIs for IBM, clients, and collaborators at MIT. As the lead, he often serves as architect for many projects and as manager for the team.