Software complexity measurement
Communications of the ACM
Software engineering metrics and models
Software engineering metrics and models
Software metrics: an overview of recent results
Journal of Systems and Software
Recent advances in software measurement (abstract and references for talk)
ICSE '90 Proceedings of the 12th international conference on Software engineering
Conceptual complexity analysis of logic programs
Conceptual complexity analysis of logic programs
Comprehending rule-based programs: a graph-oriented approach
International Journal of Man-Machine Studies
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
A Software Engineering Methodology for Rule-Based Systems
IEEE Transactions on Knowledge and Data Engineering
Conceptual complexity measures and software maintenance tools for rule-based programs
Conceptual complexity measures and software maintenance tools for rule-based programs
Software metrics: an introduction and annotated bibliography
ACM SIGSOFT Software Engineering Notes
Measuring the Maintainability of a Communication Protocol Based on Its Formal Specification
IEEE Transactions on Software Engineering
Artificial Intelligence in modelling the complexity of Mediterranean landscape transformations
Computers and Electronics in Agriculture
Evaluating agent-oriented programs: towards multi-paradigm metrics
ProMAS'10 Proceedings of the 8th international conference on Programming Multi-Agent Systems
| Hi-index | 0.00 |
Software complexity measures are quantitative estimates of the amount of effort required by a programmer to comprehend a piece of code. Many measures have been designed for standard procedural languages, but little work has been done to apply software complexity concepts to nontraditional programming paradigms. This paper presents a collection of software complexity measures that were specifically designed to quantify the conceptual complexity of rule-based programs. These measures are divided into two classes: bulk measures, which estimate complexity by examining aspects of program size, and rule measures, which gauge complexity based on the ways in which program rules interact with data and other rules. A pilot study was conducted to assess the effectiveness of these measures. Several measures were found to correlate well with the study participants' ratings of program difficulty and the time required by them to answer questions that required comprehension of program elements. The physical order of program rules was also shown to affect comprehension. The authors conclude that the development of software complexity measures for particular programming paradigms may lead to better tools for managing program development and predicting maintenance effort in nontraditional programming environments.