Metrics in the software engineering curriculum
Annals of Software Engineering - Special issue on software engineering education
Defining Metrics for Conceptual Schema Evolution
FoMLaDO/DEMM 2000 Selected papers from the 9th International Workshop on Foundations of Models and Languages for Data and Objects, Database Schema Evolution and Meta-Modeling
Research ethics and computer science: an unconsummated marriage
SIGDOC '06 Proceedings of the 24th annual ACM international conference on Design of communication
Journal of Management Information Systems
Information Sciences: an International Journal
Effective classification using feature selection and fuzzy integration
Fuzzy Sets and Systems
Quality Factors and Coding Standards -- a Comparison Between Open Source Forges
Electronic Notes in Theoretical Computer Science (ENTCS)
An information-based view of representational coupling in object-oriented systems
FASE'03 Proceedings of the 6th international conference on Fundamental approaches to software engineering
Studying software evolution using artefacts' shared information content
Science of Computer Programming
Validating software metrics: A spectrum of philosophies
ACM Transactions on Software Engineering and Methodology (TOSEM)
Performance comparison of software complexity metrics in an open source project
EuroSPI'07 Proceedings of the 14th European conference on Software Process Improvement
A quantitative assessment of aspectual feature modules for evolving software product lines
SBLP'12 Proceedings of the 16th Brazilian conference on Programming Languages
A fuzzy classifier approach to estimating software quality
Information Sciences: an International Journal
| Hi-index | 4.10 |
The software industry is an embarrassment when it comes to measurement and metrics. Many software managers and practitioners, including tenured academics in software engineering and computer science, seem to know little or nothing about these topics. Many of the measurements found in the software literature are not used with enough precision to replicate the author's findings-a canon of scientific writing in other fields. Several of the most widely used software metrics have been proved unworkable, yet they continue to show up in books, encyclopedias, and refereed journals. So long as these invalid metrics are used carelessly, there can be no true software engineering, only a kind of amateurish craft that uses rough approximations instead of precise measurement. The paper considers three significant and widely used software metrics that are invalid under various conditions: lines of code or LOC metrics, software science or Halstead metrics, and the cost-per-defect metric. Fortunately, two metrics that actually generate useful information-complexity metrics and function-point metrics-are growing in use and importance.