Authors: Lijie Zou Michael W. Godfrey
Venue: ICSME 2012 28th IEEE International Conference on Software Maintenance (ICSM), pp. 6-14, 2012
Year: 2012
Abstract: Programmers need explicit tool support for software maintenance tasks, and a prerequisite for this is an understanding of where the boundaries between distinct tasks lie. Asking developers to indicate manually when they switch tasks is disruptive to their normal work flow, so researchers have sought ways to infer task boundaries automatically based on the content of the interaction histories with the IDE. Coman previously reported a fully automated algorithm that achieved 80% accuracy in a lab validation study. In this paper, we evaluate the use of this algorithm within an industrial setting. We found two problems: first, a large number of the tasks identified are in fact only sessions or subparts of a larger task; second, the demonstrable effects of interruptions are not considered. We argue that the problem of task boundary detection consists of two sub-problems: first, detecting task sessions; and second, linking task sessions. Coman's algorithm only partially addresses the first, and ignores the second.
BibTeX:
@inproceedings{lijiezou2012aicsocatdawwwdaw,
author = "Lijie Zou and Michael W. Godfrey",
title = "An industrial case study of Coman's automated task detection algorithm: What Worked, What Didn't, and Why",
year = "2012",
pages = "6-14",
booktitle = "Proceedings of 2012 28th IEEE International Conference on Software Maintenance (ICSM)"
}
Plain Text:
Lijie Zou and Michael W. Godfrey, "An industrial case study of Coman's automated task detection algorithm: What Worked, What Didn't, and Why," 2012 28th IEEE International Conference on Software Maintenance (ICSM), pp. 6-14