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Recognizing lines of code violating company-specific coding guidelines using machine learning A Method and Its Evaluation

Journal article
Authors Miroslaw Ochodek
Regina Hebig
Wilhelm Meding
Gert Frost
Miroslaw Staron
Published in Empirical Software Engineering
Volume 25
Pages 220-265
ISSN 1382-3256
Publication year 2020
Published at Institutionen för data- och informationsteknik, Software Engineering (GU)
Software Center
Pages 220-265
Language en
Links https://doi.org/10.1007/s10664-019-...
Subject categories Software Engineering

Abstract

Software developers in big and medium-size companies are working with millions of lines of code in their codebases. Assuring the quality of this code has shifted from simple defect management to proactive assurance of internal code quality. Although static code analysis and code reviews have been at the forefront of research and practice in this area, code reviews are still an effort-intensive and interpretation-prone activity. The aim of this research is to support code reviews by automatically recognizing company-specific code guidelines violations in large-scale, industrial source code. In our action research project, we constructed a machine-learning-based tool for code analysis where software developers and architects in big and medium-sized companies can use a few examples of source code lines violating code/design guidelines (up to 700 lines of code) to train decision-tree classifiers to find similar …

Page Manager: Webmaster|Last update: 9/11/2012
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