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Open AccessProceedings Article

Application of genetic programming in software engineering empirical data modelling

TLDR
This paper demonstrates the effectiveness of the genetic programming paradigm, in two major software engineering duties, effort estimation and defect prediction, by examining data domains from both the commercial and the scientific sector, for each task.
Abstract
Research in software engineering data analysis has only recently incorporated computational intelligence methodologies. Among these approaches, genetic programming retains a remarkable position, facilitating symbolic regression tasks. In this paper, we demonstrate the effectiveness of the genetic programming paradigm, in two major software engineering duties, effort estimation and defect prediction. We examine data domains from both the commercial and the scientific sector, for each task. The proposed model is proved superior to past literature works.

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Citations
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Software engineering economics

Barry Boehm
TL;DR: In this article, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
Journal ArticleDOI

On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions

TL;DR: A systematic review of 78 primary studies from January 1992 to December 2015 which analyze the predictive capability of search-based techniques for ascertaining four predominant software quality attributes, i.e., effort, defect proneness, maintainability and change proneness is performed.
Journal ArticleDOI

Threats to validity in search-based predictive modelling for software engineering

TL;DR: This study extensively reviews 93 primary studies, which use SBAs for developing SPMs of four commonly used software attributes (effort, defect-proneness, maintainability and change- proneness) in order to discuss and identify the various sources of threats while using these approaches for S PMs.
Proceedings ArticleDOI

Common threats to software quality predictive modeling studies using search-based techniques

TL;DR: This study reviews and analyzes 33 empirical studies in literature which have successfully used search-based techniques for prediction of two common software quality attributes i.e. fault-proneness and change- proneness in order to comprehensively present various probable threats to such studies.
References
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Book

Genetic Programming: On the Programming of Computers by Means of Natural Selection

TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.

Software engineering economics

Barry Boehm
TL;DR: In this paper, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
Book

A complexity measure

TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.
Journal ArticleDOI

A Complexity Measure

TL;DR: Several properties of the graph-theoretic complexity are proved which show, for example, that complexity is independent of physical size and complexity depends only on the decision structure of a program.
Book

Software Metrics: A Rigorous and Practical Approach

TL;DR: The Second Edition of Software Metrics provides an up-to-date, coherent, and rigorous framework for controlling, managing, and predicting software development processes.
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