CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Computational models of information scent-following in a very large browsable text collection
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Using information scent to model user information needs and actions and the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SHriMP views: an interactive environment for information visualization and navigation
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Modern Information Retrieval
The bloodhound project: automating discovery of web usability issues using the InfoScentπ simulator
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hipikat: recommending pertinent software development artifacts
Proceedings of the 25th International Conference on Software Engineering
ScentTrails: Integrating browsing and searching on the Web
ACM Transactions on Computer-Human Interaction (TOCHI)
BEACONS IN PROGRAM COMPREHENSION
ACM SIGCHI Bulletin
Mylar: a degree-of-interest model for IDEs
Proceedings of the 4th international conference on Aspect-oriented software development
Towards understanding programs through wear-based filtering
SoftVis '05 Proceedings of the 2005 ACM symposium on Software visualization
Evaluating a fisheye view of source code
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Building Usage Contexts During Program Comprehension
ICPC '06 Proceedings of the 14th IEEE International Conference on Program Comprehension
Relo: Helping Users Manage Context during Interactive Exploratory Visualization of Large Codebases
VLHCC '06 Proceedings of the Visual Languages and Human-Centric Computing
Questions programmers ask during software evolution tasks
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
IEEE Transactions on Software Engineering
Using information scent to model the dynamic foraging behavior of programmers in maintenance tasks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information Foraging Theory: Adaptive Interaction with Information
Information Foraging Theory: Adaptive Interaction with Information
SNIF-ACT: a cognitive model of user navigation on the world wide web
Human-Computer Interaction
Non-programmers identifying functionality in unfamiliar code: Strategies and barriers
VLHCC '09 Proceedings of the 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
Reactive information foraging for evolving goals
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Code bubbles: a working set-based interface for code understanding and maintenance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
End-user debugging strategies: A sensemaking perspective
ACM Transactions on Computer-Human Interaction (TOCHI)
How Programmers Debug, Revisited: An Information Foraging Theory Perspective
IEEE Transactions on Software Engineering
The whats and hows of programmers' foraging diets
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Information Foraging Theory (IFT) has established itself as an important theory to explain how people seek information, but most work has focused more on the theory itself than on how best to apply it. In this paper, we investigate how to apply a reactive variant of IFT (Reactive IFT) to design IFT-based tools, with a special focus on such tools for ill-structured problems. Toward this end, we designed and implemented a variety of recommender algorithms to empirically investigate how to help people with the ill-structured problem of finding where to look for information while debugging source code. We varied the algorithms based on scent type supported (words alone vs. words + code structure), and based on use of foraging momentum to estimate rapidity of foragers' goal changes. Our empirical results showed that (1) using both words and code structure significantly improved the ability of the algorithms to recommend where software developers should look for information; (2) participants used recommendations to discover new places in the code and also as shortcuts to navigate to known places; and (3) low-momentum recommendations were significantly more useful than high-momentum recommendations, suggesting rapid and numerous goal changes in this type of setting. Overall, our contributions include two new recommendation algorithms, empirical evidence about when and why participants found IFT-based recommendations useful, and implications for the design of tools based on Reactive IFT.