C4.5: programs for machine learning
C4.5: programs for machine learning
ICSE '97 Proceedings of the 19th international conference on Software engineering
Software process validation: quantitatively measuring the correspondence of a process to a model
ACM Transactions on Software Engineering and Methodology (TOSEM)
Proceedings of the Conference on The Future of Software Engineering
Agile Software Development: Principles, Patterns, and Practices
Agile Software Development: Principles, Patterns, and Practices
Spice: The Theory and Practice of Software Process Improvement and Capability Determination
Spice: The Theory and Practice of Software Process Improvement and Capability Determination
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CMMI Guidlines for Process Integration and Product Improvement
CMMI Guidlines for Process Integration and Product Improvement
An Empirical Analysis of Productivity and Quality in Software Products
Management Science
Helping Small Companies Assess Software Processes
IEEE Software
Proceedings of the 28th international conference on Software engineering
The Detection and Classification of Non-Functional Requirements with Application to Early Aspects
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Classification of software behaviors for failure detection: a discriminative pattern mining approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Process mining framework for software processes
ICSP'07 Proceedings of the 2007 international conference on Software process
A discriminative model approach for accurate duplicate bug report retrieval
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Assessment methodology for software process improvement in small organizations
Information and Software Technology
Toward objective software process information: experiences from a case study
Software Quality Control
The prom framework: a new era in process mining tool support
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
Deviation management during process execution
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Software process evaluation: A machine learning approach
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Mining succinct predicated bug signatures
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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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.