The Visvalingam algorithm: metrics, measures and heuristics

This paper provides the background necessary for a clear understanding of forthcoming papers relating to the Visvalingam algorithm for line generalisation, for example on the testing and usage of its implementations. It distinguishes the algorithm from implementation-specific issues to explain why it is possible to get inconsistent but equally valid output from different implementations. By tracing relevant developments within the now-disbanded Cartographic Information Systems Research Group (CISRG) of the University of Hull, it explains why a) a partial metric-driven implementation was, and still is, sufficient for many projects but not for others; b) why the Effective Area (EA) is a measure derived from a metric; c) why this measure (EA) may serve as a heuristic indicator for in-line feature segmentation and model-based generalisation; and, d) how metrics may be combined to change the order of point elimination. The issues discussed in this paper also apply to the use of other metrics. It is hoped that the background and guidance provided in this paper will enable others to participate in further research based on the algorithm

Evaluating Maintainability Prejudices with a Large-Scale Study of Open-Source Projects

Exaggeration or context changes can render maintainability experience into prejudice. For example, JavaScript is often seen as least elegant language and hence of lowest maintainability. Such prejudice should not guide decisions without prior empirical validation. We formulated 10 hypotheses about maintainability based on prejudices and test them in a large set of open-source projects (6,897 GitHub repositories, 402 million lines, 5 programming languages). We operationalize maintainability with five static analysis metrics. We found that JavaScript code is not worse than other code, Java code shows higher maintainability than C# code and C code has longer methods than other code. The quality of interface documentation is better in Java code than in other code. Code developed by teams is not of higher and large code bases not of lower maintainability. Projects with high maintainability are not more popular or more often forked. Overall, most hypotheses are not supported by open-source data.Comment: 20 page