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Journal ArticleDOI

A Complexity Measure

TLDR
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.
Abstract
This paper describes a graph-theoretic complexity measure and illustrates how it can be used to manage and control program complexity. The paper first explains how the graph-theory concepts apply and gives an intuitive explanation of the graph concepts in programming terms. The control graphs of several actual Fortran programs are then presented to illustrate the correlation between intuitive complexity and the graph-theoretic complexity. Several properties of the graph-theoretic complexity are then proved which show, for example, that complexity is independent of physical size (adding or subtracting functional statements leaves complexity unchanged) and complexity depends only on the decision structure of a program.

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Citations
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Book ChapterDOI

The History of Computing Education Research

TL;DR: In the late 1960’s, researchers started gathering data, studying how learners were learning programming, when they did not, and how they experienced programming.
Proceedings ArticleDOI

What Is Complex About 273 Applications? Untangling Application Architecture Complexity in a Case of European Investment Banking

TL;DR: In this article, the authors derive propositions from the current literature addressing the causes and impacts of application architecture (AA) complexity and untangle AA complexity itself by differentiating four types of AA complexity that are mingled in extant literature: interdependency-, diversity-, deviation-, and overlap-related AA complexity.
Journal ArticleDOI

Handling Class-Imbalance with KNN (Neighbourhood) Under-Sampling for Software Defect Prediction

TL;DR: A novel Neighbourhood based Under-Sampling (N-US) algorithm to handle class imbalance issue and the performance of N-US as a trusted ally for SDP classifiers is investigated.
Journal ArticleDOI

Understanding machine learning software defect predictions

TL;DR: A tree boosting algorithm is used that receives as input a training set comprising records of easy-to-compute characteristics of each module and outputs whether the corresponding module is defect-prone, and a simple model sampling approach is proposed that finds accurate models with the minimum set of features.
Journal ArticleDOI

Technical debt and system architecture

TL;DR: This work analyzes the relationship between system architecture and maintenance costs for two software systems of similar size, but with very different structures, and finds major differences in the potential benefits available from refactoring these systems, related to their differing designs.
References
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Journal ArticleDOI

Structured Programming with go to Statements

TL;DR: For serious students of structured programming, and also for language designers, Knuth's "Structured Programming with go to Statements" is probably the paper to read.
Journal ArticleDOI

Analysis of structured programs

TL;DR: It is shown that an arbitrary program P is reducible to a D-chart if and only if P does not contain a loop with two distinct exits.
Journal ArticleDOI

A genealogy of control structures

TL;DR: It is argued further that there is insufficient evidence to warrant more than if-then-else, while-do, and their variants and the importance of one-in, one-out control structures as operational abstractions.