IBM Systems Journal
Multi-View Software Evolution: A UML-based Framework for Evolving Object-Oriented Software
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Determinants of software volatility: a field study
Journal of Software Maintenance: Research and Practice
Creating a cognitive metric of programming task difficulty
Proceedings of the 2008 international workshop on Cooperative and human aspects of software engineering
On the complexity of measuring software complexity
AFIPS '81 Proceedings of the May 4-7, 1981, national computer conference
A Reconfigurable Architecture for Building Intelligent Learning Environments
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
An evaluation of the internal quality of business applications: does size matter?
Proceedings of the 33rd International Conference on Software Engineering
Proceedings of the 7th International Workshop on Software Engineering for Secure Systems
A paradigm comparison for collecting TV channel statistics from high-volume channel zap events
Proceedings of the 5th ACM international conference on Distributed event-based system
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
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Three software complexity measures (Halstead's E, McCabe's u(G), and the length as measured by number of statements) were compared to programmer performance on two software maintenance tasks. In an experiment on understanding, length and u(G) correlated with the percent of statements correctly recalled. In an experiment on modification, most significant correlations were obtained with metrics computed on modified rather than unmodified code. All three metrics correlated with both the accuracy of the modification and the time to completion. Relationships in both experiments occurred primarily in unstructured rather than structured code, and in code with no comments. The metrics were also most predictive of performance for less experienced programmers. Thus, these metrics appear to assess psychological complexity primarily where programming practices do not provide assistance in understanding the code.