Heather M. Conboy

Senior Research Fellow

Manning College of Information and Computer Sciences

University of Massachusetts Amherst

Curriculum Vitae

Contact Information

140 Governors Drive
Amherst, MA 01003-9264

Email: hconboy@cs.umass.edu
Office: 314 Computer Science Building



I work in the Laboratory for Software Engineering Research (LASER). My research interests are in the areas of requirements engineering, static analysis, and real-time monitoring/guidance.

The goal of my PhD research has been to investigate techniques to improve the quality of human-intensive processes (HIPs) (e.g., medical procedures) that involve coordination among human performers, software applications, and hardware devices. Since the HIPs are often complex involving aspects such as concurrency and exceptional situations, I have developed static analysis techniques that help prevent the HIPs from violating their overall system requirements and evaluated these techniques on case studies.

I contributed to the FLAVERS model checker that employs static data flow analysis techniques to verify whether or not all potential traces through a finite-state model of a system satisfy a given requirement of that system. If not, this model checker can generate a counterexample trace that illustrates a potential system requirement violation to help with program understanding and debugging.

Additionally, I investigated a static analysis approach to automatically derived requirements for components used in HIPs based on an interface synthesis method developed for software systems. The derived requirements are represented as finite state automata that prevent any sequences of procedure calls to a selected component that violate a given system requirement. The requirement derivation employs a learning algorithm to iteratively refine the automaton based on counterexamples traces generated by a model checker. Such requirements can be used to select the right process for a given component or alternatively to select the right component for a given process.

Checklists that guide the human performers though on-going processes have been applied in multiple domains and shown to reduce human errors. I am also contributing to a dynamic approach that generates context-sensitive Smart Checklists by matching validated HIP models against streams of real-world process activity events.


Most of my publications are available from the LASER publication database.

Refereed Papers

Invited Talks and Posters

Software Tools and Benchmarks