Measures of testability as a basis for quality assurance
Software Engineering Journal
Using test case metrics to predict code quality and effort
ACM SIGSOFT Software Engineering Notes
How many paths are needed for branch testing?
Journal of Systems and Software - Special issue on software reliability issues
A case study in branch testing automation
Journal of Systems and Software - Special issue on achieving quality in software
Refactoring: improving the design of existing code
Refactoring: improving the design of existing code
Software product-line engineering: a family-based software development process
Software product-line engineering: a family-based software development process
Testing object-oriented systems: models, patterns, and tools
Testing object-oriented systems: models, patterns, and tools
Software Metrics: A Rigorous Approach
Software Metrics: A Rigorous Approach
ICSE '76 Proceedings of the 2nd international conference on Software engineering
Predicting Class Testability using Object-Oriented Metrics
SCAM '04 Proceedings of the Source Code Analysis and Manipulation, Fourth IEEE International Workshop
Effective test metrics for test strategy evolution
CASCON '04 Proceedings of the 2004 conference of the Centre for Advanced Studies on Collaborative research
Methodology for the Generation of Program Test Data
IEEE Transactions on Computers
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In order to support an iterative architecture and code improvement process a dedicated code analysis tool has been developed. But introducing the process and the tool in a medium sized company is always accompanied by difficulties, like understanding improvement needs. We therefore decided to use test effort as the central communication metaphor for code complexity. Hence, we developed a metric suite to calculate the number of test cases needed for branch coverage and (modified) boundary interior test. This paper introduces the developed metrics and also presents a case study performed at a medium sized software company to evaluate our approach. The main part of this paper is dedicated to the interpretation and comparison of the metrics. Finally their application in an iterative code improvement process is investigated.