Please use this identifier to cite or link to this item:
Title: Mining explicit rules for software process evaluation
Authors: Sun, C.
Du, J.
Chen, N.
Khoo, S.-C. 
Yang, Y.
Keywords: Contrasting rule mining
Software process evaluation
Issue Date: 2013
Source: Sun, C.,Du, J.,Chen, N.,Khoo, S.-C.,Yang, Y. (2013). Mining explicit rules for software process evaluation. ACM International Conference Proceeding Series : 118-125. ScholarBank@NUS Repository.
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. Copyright 2013 ACM.
Source Title: ACM International Conference Proceeding Series
ISBN: 9781450320627
DOI: 10.1145/2486046.2486067
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Feb 21, 2018

Page view(s)

checked on Feb 24, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.