The C programming language
Modeling and forecasting the information sciences
Information Sciences: an International Journal - Special issue on information sciences—past, present, and future
Rationale for the design of the Ada programming language
Rationale for the design of the Ada programming language
HOPL-II The second ACM SIGPLAN conference on History of programming languages
Software technology maturation
ICSE '85 Proceedings of the 8th international conference on Software engineering
Diffusing Software-Engineering Methods
IEEE Software
IEEE Software
Software Engineering Technology Watch
IEEE Software
An Empirical Study of Programming Language Trends
IEEE Software
Modeling the evolution of operating systems: An empirical study
Journal of Systems and Software
The FORTRAN automatic coding system
IRE-AIEE-ACM '57 (Western) Papers presented at the February 26-28, 1957, western joint computer conference: Techniques for reliability
Estimating software readiness using predictive models
Information Sciences: an International Journal
Software engineering technology innovation - Turning research results into industrial success
Journal of Systems and Software
Improving the efficiency of use of software engineering practices using product patterns
Information Sciences: an International Journal
Personalized recommendation of popular blog articles for mobile applications
Information Sciences: an International Journal
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Many decision-makers in industry, government and academia routinely make decisions whose outcome depends on the evolution of software technology trends. Even though the stakes of these decisions are usually very high, decision makers routinely depend on expert opinions and qualitative assessments to model the evolution of software technology; both of these sources of decision-making information are subjective, are based on opinions rather than facts, and are prone to error. In this paper, we report on our ongoing work to build quantitative models of the evolution of software technology trends. In particular, we discuss how we took specific evolutionary models and merged them into a single (general-purpose) model. The original specific models are derived empirically using statistical methods on trend data we had collected over several years, and have been validated individually; in this paper we further validate the generic (general-purpose) model.