Object-oriented modeling and design
Object-oriented modeling and design
Recovering software specifications with inductive logic programming
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
SSR '95 Proceedings of the 1995 Symposium on Software reusability
Artificial Intelligence
Rules and strategies for transforming functional and logic programs
ACM Computing Surveys (CSUR)
Machine learning from examples: inductive and lazy methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Partial evaluation of functional logic programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
Social processes and proofs of theorems and programs
Communications of the ACM
An axiomatic basis for computer programming
Communications of the ACM
Adaptive Object-Oriented Software: The Demeter Method with Propagation Patterns
Adaptive Object-Oriented Software: The Demeter Method with Propagation Patterns
Machine Learning
Automated Software Engineering
The Case for Inductive Programming
Computer
Guest Editors' Introduction: Requirements Engineering
IEEE Software
Machine Learning
Machine Learning
The Experimental Paradigm in Software Engineering
Proceedings of the International Workshop on Experimental Software Engineering Issues: Critical Assessment and Future Directions
Chapter I: Notes on structured programming
Structured programming
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
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In this paper Software Development (SD) is understood explicitly as a learning process, which relies much more on induction than deduction, with the main goal of being predictive to requirements evolution. Concretely, classical processes from philosophy of science and machine learning such as hypothesis generation, refinement, confirmation and revision have their counterpart in requirement engineering, program construction, validation and modification in SD, respectively. Consequently, we have investigated the appropriateness for software modelling of the most important paradigms of modelling selection in machine learning. Under the notion of incremental learning, we introduce a new factor, predictiveness, as the ability to foresee future changes in the specification, thereby reducing the number of revisions. As a result, other quality factors are revised. Finally, a predictive software life cycle is outlined as an incremental learning session, which may or may not be automated.