Open Access
Predicting Source Code Quality with Static Analysis and Machine Learning
Vera Barstad,Morten Goodwin,Terje Gjøsæter +2 more
- 01 Jan 2014
TL;DR: This paper is investigating if it is possible to predict source code quality based on static analysis and machine learning, and uses a combination of peer review/human rating, static code analysis, and classification methods.
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Abstract: This paper is investigating if it is possible to predict source code quality based on static analysis and machine learning. The proposed approach includes a plugin in Eclipse, uses a combination of peer review/human rating, static code analysis, and classification methods. As training data, public data and student hand-ins in programming are used. Based on this training data, new and uninspected source code can be accurately classified as “well written” or “badly written”. This is a step towards feedback in an interactive environment without peer assessment.
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Citations
A GQM-based Method and a Bayesian Approach for the Detection of Code and Design Smells
Yann-Gaël Guéhéneuc
- 01 Jan 2011
TL;DR: In this paper, a probabilistic model is proposed to detect occurrences of the Blob antipattern in code and design smells in programs, which can be calibrated using machine learning techniques to offer an improved, context-specific detection.
165
Evaluation of Machine Learning Approaches for Change-Proneness Prediction Using Code Smells
Kamaldeep Kaur,Shilpi Jain +1 more
- 01 Jan 2017
TL;DR: This paper concludes that gene expression programming (GEP) gives maximum AUC value, whereas cascade correlation network (CCR), treeboost, and PNN\GRNN algorithms are among top algorithms to predict F-measure, precision, recall, and accuracy.
11
•Posted Content
DevSecOps in Robotics.
TL;DR: DevSecOps in Robotics is introduced, a set of best practices designed to help roboticists implant security deep in the heart of their development and operations processes.
Workflow analysis of data science code in public GitHub repositories
TL;DR: In this article , the authors analyzed 470 Jupyter notebooks publicly available in GitHub repositories to understand how data scientists transition between different types of data science activities, or steps (such as data preprocessing and modelling), as well as the frequency and cooccurrence of such transitions.
Input Validation Vulnerabilities in Web Applications: Systematic Review, Classification, and Analysis of the Current State-of-the-Art
01 Jan 2023
TL;DR: In this paper , a new classification for web application input validation vulnerabilities is proposed and various techniques/tools that are used to detect them are analyzed and evaluated to apprehend their strengths and weaknesses.
References
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
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.
6K
•Book
A complexity measure
Thomas J. McCabe
- 04 Oct 1993
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.
5.1K
N-gram-based text categorization
W.B. Cavnar,John M. Trenkle +1 more
- 31 Dec 1994
TL;DR: An N-gram-based approach to text categorization that is tolerant of textual errors is described, which worked very well for language classification and worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject.