Amber Horvath - Meta-Information to Support Sensemaking by Developers
Speaker
Amber Horvath
CSAIL
Host
Arvind Satyanarayan
CSAIL
Abstract:
Software development requires developers to juggle and balance many information-seeking and understanding tasks. From determining how a bug was introduced, to choosing what API method to use to resolve the bug, to how to properly integrate this change, even the smallest implementation tasks can lead to many questions. These questions may range from hard-to-answer questions about the rationale behind the original code to common questions such as how to use an API. Once this challenging sensemaking is done, this rich thought history is often lost given the high cost of externalizing these details, despite potentially being useful to future developers. In this talk, I discuss the design principles necessary to capture and make useful this rich set of data and the different systems I have developed that instantiate these principles. Specifically, I have developed systems for annotating to support developers’ natural sensemaking when understanding information-dense sources such as software documentation and source code. I then demonstrated how to automate and scale the capturing of other forms of meta-information to assist with reasoning about design. Lastly, I explored how this information can be utilized by LLMs to assist in the applied developer sensemaking task of print debugging. In looking towards the future of developer information needs, I discuss how these processes and systems may change to adapt to the new classes of information needs that the shift towards AI-driven software engineering are creating.
Bio:
Amber Horvath is a post-doctoral researcher at the Massachusetts Institute of Technology, working with Arvind Satyanarayan and David Karger. She received her Ph.D. from Carnegie Mellon University in the Human-Computer Interaction Institute, where she was advised by Brad Myers. She works at the intersection of human-computer interaction (HCI), software engineering, and applied AI. She uses human-centered methods to design and build novel tools to help developers better manage their information. She has also done work related to fostering more inclusive environments for underrepresented populations in computing, using novel methodologies and large-scale data analysis. She publishes at premier venues in the fields of HCI and software engineering, including CHI, UIST, ICSE, and CSCW, with award-winning papers at CHI and CSCW.
This talk will also be streamed over Zoom: https://mit.zoom.us/j/98354678322.
Software development requires developers to juggle and balance many information-seeking and understanding tasks. From determining how a bug was introduced, to choosing what API method to use to resolve the bug, to how to properly integrate this change, even the smallest implementation tasks can lead to many questions. These questions may range from hard-to-answer questions about the rationale behind the original code to common questions such as how to use an API. Once this challenging sensemaking is done, this rich thought history is often lost given the high cost of externalizing these details, despite potentially being useful to future developers. In this talk, I discuss the design principles necessary to capture and make useful this rich set of data and the different systems I have developed that instantiate these principles. Specifically, I have developed systems for annotating to support developers’ natural sensemaking when understanding information-dense sources such as software documentation and source code. I then demonstrated how to automate and scale the capturing of other forms of meta-information to assist with reasoning about design. Lastly, I explored how this information can be utilized by LLMs to assist in the applied developer sensemaking task of print debugging. In looking towards the future of developer information needs, I discuss how these processes and systems may change to adapt to the new classes of information needs that the shift towards AI-driven software engineering are creating.
Bio:
Amber Horvath is a post-doctoral researcher at the Massachusetts Institute of Technology, working with Arvind Satyanarayan and David Karger. She received her Ph.D. from Carnegie Mellon University in the Human-Computer Interaction Institute, where she was advised by Brad Myers. She works at the intersection of human-computer interaction (HCI), software engineering, and applied AI. She uses human-centered methods to design and build novel tools to help developers better manage their information. She has also done work related to fostering more inclusive environments for underrepresented populations in computing, using novel methodologies and large-scale data analysis. She publishes at premier venues in the fields of HCI and software engineering, including CHI, UIST, ICSE, and CSCW, with award-winning papers at CHI and CSCW.
This talk will also be streamed over Zoom: https://mit.zoom.us/j/98354678322.