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Title:
Mining explicit rules for software process evaluation
Author: Sun, Chengnian (1) ; Du, Jing (2) ; Chen, Ning (1) ; Khoo, Siau-Cheng (1) ; Yang, Ye (2)
Conference Name: 2013 International Conference on Software and Systems Process, ICSSP 2013
Conference Date: May 18, 2013 - May 19, 2013
Issued Date: 2013
Conference Place: San Francisco, CA, United states
Publish Place: Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States
Indexed Type: EI
ISBN: 9781450320627
Department: (1) School of Computing, National University of Singapore, Singapore; (2) Lab for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, China
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.
English 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16645
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Sun, Chengnian ,Du, Jing ,Chen, Ning ,et al. Mining explicit rules for software process evaluation[C]. 见:2013 International Conference on Software and Systems Process, ICSSP 2013. San Francisco, CA, United states. May 18, 2013 - May 19, 2013.
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