Metrics That Matter

Authors:
Tim Menzies;Justin S. Di Stefano;Mike Chapman;Ken McGill
Affiliations:
-;-;-;-
Venue:
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
Year:
2002

Citing 6
Cited 8

A critique of cyclomatic complexity as a software metric

Software Engineering Journal
Design complexity measurement and testing

Communications of the ACM
Software Metrics: A Rigorous and Practical Approach

Software Metrics: A Rigorous and Practical Approach
Elements of Software Science (Operating and programming systems series)

Elements of Software Science (Operating and programming systems series)
Complexity Measure Evaluation and Selection

IEEE Transactions on Software Engineering
A Complexity Measure

IEEE Transactions on Software Engineering

Data Mining Static Code Attributes to Learn Defect Predictors

IEEE Transactions on Software Engineering
Implications of ceiling effects in defect predictors

Proceedings of the 4th international workshop on Predictor models in software engineering
A defect prediction method for software versioning

Software Quality Control
Software metrics reduction for fault-proneness prediction of software modules

NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Defect prediction using social network analysis on issue repositories

Proceedings of the 2011 International Conference on Software and Systems Process
Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Advances in Engineering Software
The design of polynomial function-based neural network predictors for detection of software defects

Information Sciences: an International Journal
Source code size estimation approaches for object-oriented systems from UML class diagrams: A comparative study

Information and Software Technology

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Abstract

Within NASA, there is an increasing awareness that software is of growing importance to the success of missions. Much data has been collected, and many theories have been advanced on how to reduce or eliminate errors in code. However, learning requires experience. This article documents a new NASA initiative to build a centralized repository of software defect data; in particular, it documents one specific case study on software metrics. Software metricsare used as a basis for prediction of errors in code modules, but there are many different metrics available. McCabe is one of the more popular tools used to produce metrics, but, as will be shown in this paper, other metrics can be more significant.