C4.5: programs for machine learning
C4.5: programs for machine learning
ICSE '97 Proceedings of the 19th international conference on Software engineering
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
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
CMMI Guidlines for Process Integration and Product Improvement
CMMI Guidlines for Process Integration and Product Improvement
From run-time behavior to usage scenarios: an interaction-pattern mining approach
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Efficient mining of iterative patterns for software specification discovery
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Intrusion detection using sequences of system calls
Journal of Computer Security
The impact of process choice in high maturity environments: An empirical analysis
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Data Mining for Software Engineering
Computer
Process mining framework for software processes
ICSP'07 Proceedings of the 2007 international conference on Software process
Sequence Data Mining
Assessment methodology for software process improvement in small organizations
Information and Software Technology
A brief survey on sequence classification
ACM SIGKDD Explorations Newsletter
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
Mining explicit rules for software process evaluation
Proceedings of the 2013 International Conference on Software and System Process
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Software process evaluation is essential to improve software development and the quality of software products in an organization. Conventional approaches based on manual qualitative evaluations (e.g., artifacts inspection) are deficient in the sense that (i) they are time-consuming, (ii) they suffer from the authority constraints, and (iii) they are often subjective. To overcome these limitations, this paper presents a novel semi-automated approach to software process evaluation using machine learning techniques. In particular, we formulate the problem as a sequence classification task, which is solved by applying machine learning algorithms. Based on the framework, we define a new quantitative indicator to objectively evaluate the quality and performance of a software process. To validate the efficacy of our approach, we apply it to evaluate the defect management process performed in four real industrial software projects. Our empirical results show that our approach is effective and promising in providing an objective and quantitative measurement for software process evaluation.