TaskTracer: a desktop environment to support multi-tasking knowledge workers
Proceedings of the 10th international conference on Intelligent user interfaces
Building Usage Contexts During Program Comprehension
ICPC '06 Proceedings of the 14th IEEE International Conference on Program Comprehension
Using task context to improve programmer productivity
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Automatically finding and recommending resources to support knowledge workers' activities
Proceedings of the 13th international conference on Intelligent user interfaces
Automated Identification of Tasks in Development Sessions
ICPC '08 Proceedings of the 2008 The 16th IEEE International Conference on Program Comprehension
A Lightweight Approach for Knowledge Sharing in Distributed Software Teams
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
Detecting and correcting user activity switches: algorithms and interfaces
Proceedings of the 14th international conference on Intelligent user interfaces
From work to word: How do software developers describe their work?
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Assisting engineers in switching artifacts by using task semantic and interaction history
Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
How do professional developers comprehend software?
Proceedings of the 34th International Conference on Software Engineering
How do professional developers comprehend software?
Proceedings of the 34th International Conference on Software Engineering
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Detecting the current activity of developers and problems they are facing is a prerequisite for a context-aware assistance and for capturing developersâ聙聶 experiences during their work. We present an approach to detect the current activity of software developers and if they are facing a problem. By observing developer actions like changing code or searching the web, we detect whether developers are locating the cause of a problem, searching for a solution, or applying a solution. We model development work as recurring problem solution cycle, detect developerâ聙聶s actions by instrumenting the IDE, translate developer actions to observations using ontologies, and infer developer activities by using Hidden Markov Models. In a preliminary evaluation, our approach was able to correctly detect 72% of all activities. However, a broader more reliable evaluation is still needed.