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37 Publications visible to you, out of a total of 37
Is there a Correlation between the Use of Swearwords and Code Quality in Open Source Code?

Abstract (Expand)

One of the most fundamental unanswered questions that has been bothering mankind during the Anthropocene is whether the use of swearwords in open source code is positively or negatively correlated with … source code quality. To investigate this profound matter we crawled and analysed over 3800 C open source code containing English swearwords and over 7600 C open source code not containing swearwords from GitHub. Subsequently, we quantified the adherence of these two distinct sets of source code to coding standards, which we deploy as a proxy for source code quality via the SoftWipe tool developed in our group. We find that open source code containing swearwords exhibit significantly better code quality than those not containing swearwords under several statistical tests. We hypothesise that the use of swearwords constitutes an indicator of a profound emotional involvement of the programmer with the code and its inherent complexities, thus yielding better code based on a thorough, critical, and dialectic code analysis process.

Authors: Jan Strehmel, Ben Bettisworth, Dimitri Höhler, Alexandros Stamatakis

Date Published: 1st Feb 2023

Publication Type: Bachelor's Thesis

From Easy to Hopeless—Predicting the Difficulty of Phylogenetic Analyses

Abstract (Expand)

Abstract Phylogenetic analyzes under the Maximum-Likelihood (ML) model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. …ultiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating ML-based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyzes, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.

Authors: Julia Haag, Dimitri Höhler, Ben Bettisworth, Alexandros Stamatakis

Date Published: 1st Dec 2022

Publication Type: Journal

The Free Lunch is not over yet – Systematic Exploration of Numerical Thresholds in Phylogenetic Inference

Abstract

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Authors: Julia Haag, Lukas Hübner, Alexey M. Kozlov, Alexandros Stamatakis

Date Published: 14th Jul 2022

Publication Type: Journal

From Easy to Hopeless - Predicting the Difficulty of Phylogenetic Analyses

Abstract

Not specified

Authors: Julia Haag, Dimitri Höhler, Ben Bettisworth, Alexandros Stamatakis

Date Published: 21st Jun 2022

Publication Type: Journal

Metagenomic Analysis Using Phylogenetic Placement—A Review of the First Decade

Abstract

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Authors: Lucas Czech, Alexandros Stamatakis, Micah Dunthorn, Pierre Barbera

Date Published: 26th May 2022

Publication Type: Journal

Lagrange-NG: The next generation of Lagrange

Abstract

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Authors: Ben Bettisworth, Stephen A. Smith, Alexandros Stamatakis

Date Published: 20th Apr 2022

Publication Type: Journal

SPART: A versatile and standardized data exchange format for species partition information

Abstract

Not specified

Authors: Aurélien Miralles, Jacques Ducasse, Sophie Brouillet, Tomas Flouri, Tomochika Fujisawa, Paschalia Kapli, L. Lacey Knowles, Sangeeta Kumari, Alexandros Stamatakis, Jeet Sukumaran, Sarah Lutteropp, Miguel Vences, Nicolas Puillandre

Date Published: 2022

Publication Type: Journal

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