Measuring the Ripple Effect of Pascal Programs
IWSM '00 Proceedings of the 10th International Workshop on New Approaches in Software Measurement
Deriving an approximation algorithm for automatic computation of ripple effect measures
Information and Software Technology
Ripple effect in object oriented programs
Journal of Computational Methods in Sciences and Engineering - Selected papers from the International Conference on Computer Science, Software Engineering, Information Technology, e-Business, and Applications, 2004
Quantitatively measuring object-oriented couplings
Software Quality Control
Maintenance as a function of design
AFIPS '84 Proceedings of the July 9-12, 1984, national computer conference and exposition
Software productivity measurement
AFIPS '83 Proceedings of the May 16-19, 1983, national computer conference
Determining factors that affect long-term evolution in scientific application software
Journal of Systems and Software
Software clustering based on behavioural features
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
Science of Computer Programming
Minimizing the ripple effect of web-centric software by using the pheromone extension
Information Sciences: an International Journal
Towards evolvable software architectures based on systems theoretic stability
Software—Practice & Experience
Journal of Systems and Software
COMPLEXITY MEASURES FOR NETWORK PROCESS EVOLUTION
Journal of Integrated Design & Process Science
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Software maintenance is the dominant factor contributing to the high cost of software. In this paper, the software maintenance process and the important software quality attributes that affect the maintenance effort are discussed. One of the most important quality attributes of software maintainability is the stability of a program, which indicates the resistance to the potential ripple effect that the program would have when it is modified. Measures for estimating the stability of a program and the modules of which the program is composed are presented, and an algorithm for computing these stability measures is given. An algorithm for normalizing these measures is also given. Applications of these measures during the maintenance phase are discussed along with an example. An indirect validation of these stability measures is also given. Future research efforts involving application of these measures during the design phase, program restructuring based on these measures, and the development of an overall maintainability measure are also discussed.