Authors: Chengnian Sun Jing Du Ning Chen Siau-Cheng Khoo Ye Yang
Venue: 2013 International Conference on Software and System Process, pp. 118-125, 2013
Year: 2013
Abstract: We present an approach to automatically discovering explicit rules for software process evaluation from evaluation histories. Each rule is a conjunction of a subset of attributes in a process execution, characterizing why the execution is normal or anomalous. The discovered rules can be used for stakeholder as expertise to avoid mistakes in the future, thus improving software process quality; it can also be used to compose a classifier to automatically evaluate future process execution. We formulate this problem as a contrasting itemset mining task, and employ the branch-and-bound technique to speed up mining by pruning search space. We have applied the proposed approach to four real industrial projects in a commercial bank. Our empirical studies show that the discovered rules can precisely pinpoint the cause of all anomalous executions, and the classifier built on the rules is able to accurately classify unknown process executions into the normal or anomalous class.
BibTeX:
@inproceedings{chengniansun2013merfspe,
author = "Chengnian Sun and Jing Du and Ning Chen and Siau-Cheng Khoo and Ye Yang",
title = "Mining Explicit Rules for Software Process Evaluation",
year = "2013",
pages = "118-125",
booktitle = "Proceedings of the 2013 International Conference on Software and System Process"
}
Plain Text:
Chengnian Sun, Jing Du, Ning Chen, Siau-Cheng Khoo, and Ye Yang, "Mining Explicit Rules for Software Process Evaluation," 2013 International Conference on Software and System Process, pp. 118-125